Elsevier

Ecological Indicators

Volume 142, September 2022, 109203
Ecological Indicators

Original Articles
Factors affecting the presence of Arctic charr in streams based on a jittered binary genetic programming model

https://doi.org/10.1016/j.ecolind.2022.109203Get rights and content
Under a Creative Commons license
open access

Highlights

  • A novel evolutionary model (JBGP) was developed.

  • The JBGP classifies the presence of Arctic charr in the Teno River catchment.

  • The JBGP is superior to the jittered decision tree model.

  • Macroinvertebrates, mires and slope are the dominant indicators.

Abstract

Arctic charr is one of the fish species most sensitive to climate change but studies on their freshwater habitat preferences are limited, especially in the fluvial environment. Machine learning methods offer automatic and objective models for ecohydrological processes based on observed data. However, i) the number of ecological records is often much smaller than hydrological observations, and ii) ecological measurements over the long-term are costly. Consequently, ecohydrological datasets are scarce and imbalanced. To address these problems, we propose jittered binary genetic programming (JBGP) to detect the most dominant ecohydrological parameters affecting the occurrence of Arctic charr across tributaries within the large subarctic Teno River catchment, in northernmost Finland and Norway. We quantitatively assessed the accuracy of the proposed model and compared its performance with that of classic genetic programming (GP), decision tree (DT) and state-of-the-art jittered-DT methods. The JBGP achieves the highest total classification accuracy of 90% and a Heidke skill score of 78%, showing its superiority over its counterparts. Our results showed that the dominant factors contributing to the presence of Arctic charr in Teno River tributaries include i) a higher density of macroinvertebrates, ii) a lower percentage of mires in the catchment and iii) a milder stream channel slope.

Keywords

Ecohydrological modelling
Scarce data
Genetic Programming
Arctic Charr
Jittering

Data availability

Data will be made available on request.

Cited by (0)