Arthur Compin

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 article, spatial patterns of freshwater fish species at regional and local scales were investigated to explore the possible role of interspecific interactions in influencing distribution and abundance within communities occupying coastal streams of North-Western France.

TL;DR: In this paper, the authors analyzed the regional distribution of 283 lotic macroinvertebrate species from four insect orders (Ephemeroptera, Plecoptera, Trichoptera and Coleoptera =EPTC) in the Adour-Garonne drainage basin (South-Western France, surface =116 000 km 2 ).

TL;DR: In this paper, Artificial Neural Networks (ANNs) were used to quantify the distribution of macroinvertebrate functional feeding groups (FFGs) in relation to physical variables and to land-cover in the Adour-Garonne stream system.

TL;DR: A multilayer perceptron neural network, trained using the backpropagation algorithm, was used to predict EPTC richness (output) using the 4 above-mentioned environmental variables (input) and showed high predictability; a sensitivity analysis revealed that elevation and stream order contributed the most among the 4 input variables.