Régis Céréghino

TL;DR: In this article, two artificial neural networks (ANNs), unsupervised and supervised learning algorithms, were applied to suggest practical approaches for the analysis of ecological data, and the results suggested that methodologies successively using two different neural networks are helpful to understand ecological data through ordination first, and then to predict target variables.

TL;DR: In this paper, the authors explore scientific problems to be solved in order to increase the understanding and the protection of ponds, to highlight those aspects of pond ecology that are relevant to freshwater science, and to bring out research areas which are likely to prove fruitful for further investigation.

TL;DR: It is concluded that different levels of expertise represent an opportunity for stimulating cross-fertilisation in the vast field of water research rather than simply yielding a collection of case studies to be re-examined.

TL;DR: While the ecological role of ponds is now well-established, authoritative research-based advice remains needed to inform future direction in the conservation of small water bodies and to further bridge the gap between science and practice.