English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Artificial Neural Network models (ANNs) were used to predict habitat suitability for 12 macroinvertebrate taxa, using environmental input variables. This modelling technique was applied to a dataset of 102 measurement series collected in 31 sampling sites in the Greek river Axios. The database consisted of seven physical-chemical and seven structural variables, as well as abundances of 90 macroinvertebrate taxa. A seasonal variable was included to allow the description of potential temporal changes in the macroinvertebrate communities. The induced models performed well for predicting habitat suitability of the macroinvertebrate taxa. Senso-nets and sensitivity analyses revealed that dissolved oxygen concentration and the substrate composition always played a crucial role in predicting habitat suitability of the macroinvertebrates. Although ANNs are often referred to as black box prediction techniques, it was demonstrated that ANNs combined with sensitivity analyses can provide insight in the relationship between river conditions and the occurrence of macroinvertebrates, and thus deliver new ecological knowledge. Consequently, these models can be useful in decision-making for river restoration and conservation management.

Development of artificial neural network models predicting macroinvertebrate taxa in the river Axios (Northern Greece)