R2.1 Machine Learning Approaches
R2.1 _ Understanding species distribution, population dynamics and phenology by machine learning. (Session Chairs: Antonino Staiano, Alberto Lanzoni Friedrich Recknagel)
Inferential modelling by machine learning techniques allows efficient and user-friendly analysis and synthesis of highly complex ecological data. Methods like random forest, quantile regression forest, Maxent, GARP have been successfully applied for species distribution modelling of extensive spatial data resulting in species response curves that describe a species’ response to a given habitat condition, and illustrate specific habitat requirements for the species. Non-supervised artificial neural networks, regression trees and canonical correspondence analyses allow to ordinate and classify complex ecological data. Applications of support vector machines, supervised artificial neural networks and evolutionary algorithms to large spatial and temporal data allow to predict population dynamics and reveal phenology.
This session welcomes papers on all aspects of inferential modelling of ecological data by means of novel machine learning techniques.
Overall and site-specific response of the macroinvertebrate community of Swan Coastal Plain Wetlands (West Australia) to water quality gradients revealed by GF and HEA
Jawairia Sultana, Friedrich Recknagel, Jennifer A. Davis and Bruce C. Chessman
Community and Population Abundance Patterns in Benthic Macroinvertebrates in Streams Unraveled by Species Abundance Distribution and Machine Learning
Tae-Soo Chon, Kyu-Suk Kwak, Yong-Hyuck Jang, Jaehan Choi and Joo-Baek Leem
A mixed model approach to modelling global habitat suitability and invasion risk of the American bullfrog
Desiree Andersen and Yikweon Jang
Testing the strengths of relationships between otter populations, fish and macroinvertebrate communities as well as habitat conditions across three Korean rivers by inferential modelling based on the hybrid evolutionary algorithm HEA
Sungwon Hong, Friedrich Recknagel, Tae-Soo Chon and Gea-Jae Joo
Performance evaluation and hyperparameter tuning of statistical and machine-learning models using spatial data
Patrick Schratz, Jannes Muenchow, Jakob Richter, Eugenia Iturritxa and Alexander Brenning
Statistically reinforced machine learning for nonlinear interactions of factors and hierarchically nested spatial patterns
Masahiro Ryo and Matthias C. Rillig