Proceedings of the 10th International Conference on Ecological Informatics Translating Ecological Data into Knowledge and Decisions in a Rapidly Changing World. 24-28 September 2018, Jena, Germany Proceedings of the 10th International Conference on Ecological Informatics PROCEEDINGS TH 10 INTERNATIONAL CONFERENCE ON ECOLOGICAL INFORMATICS Translating Ecological Data into Knowledge and Decisions in a Rapidly Changing World 24-28 September 2018, Jena, Germany Edited by- Jitendra Gaikad1,2, Birgitta König-Ries1,2, and Friedrich Recknagel3 1 Friedrich Schiller University Jena, Germany; 2 German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Germany; 3 University of Adelaide, Australia Disclaimer The ideas and opinions expressed in this document are those of the authors and do not necessarily represents the views of the Friedrich Schiller University, iDiv, University of Adelaide and Journal of Ecological Informatics. The authors are fully responsible for the submitted material (abstracts) in this document and should be contacted directly for further information as preferred. The proceedings should be cited as: Gaikwad, J., König-Ries, B., & Recknagel, F. (Eds). Proceedings of the ‘10th International Conference on Ecological Informatics: translating ecological data into knowledge and decisions in a rapidly changing world’, Jena, Germany, 24-28 September, 2018. This document is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ or send a letter to Creative Commons, PO Box 1866, Mountain View, CA 94042, USA. Page 1 of 299 th th 24 - 28 September 2018 https://icei2018.uni-jena.de Proceedings of the 10th International Conference on Ecological Informatics Preface It is with great pleasure to welcome you to the 10th International Conference on Ecological Informatics hosted by the Friedrich Schiller University, Jena, Germany. The Conference Proceedings are an impressive display of the current scope of Ecological Informatics as schematically represented in Figure. 1. Whilst Data Management, Analysis, Synthesis and Forecasting have been lasting popular themes over the past nine biannual ICEI conferences, ICEI 2018 addresses distinctively novel developments in Data Acquisition enabled by cutting edge in situ and remote sensing technology. The here presented ICEI 2018 program captures well current trends and challenges of Ecological Informatics towards: • regional, continental and global sharing of ecological data, • thorough integration of complementing monitoring technologies including DNA-barcoding, • sophisticated pattern recognition by deep learning, • advanced exploration of valuable information in ‘big data’ by means of machine learning and process modelling, • decision-informing solutions for biodiversity conservation and sustainable ecosystem management in light of global changes. Figure. 1: Scope of Ecological Informatics I herewith wish all delegates an inspiring week of science, communication and socialising. Adelaide, 5th September 2018 Friedrich Recknagel Scientific Program Chair ICEI 2018 University of Adelaide AUSTRALIA Page 2 of 299 th th 24 - 28 September 2018 https://icei2018.uni-jena.de Proceedings of the 10th International Conference on Ecological Informatics Table of Content Session R1.1 ................................................................................................................................................... 14 Ecological monitoring by camera, thermal and acoustic images..................................................................... 15 Keeping the Human in the Loop: Towards Automatic Visual Monitoring in Biodiversity Research ................ 16 Automating biological monitoring on the Northern Andes of South America: combining biology and machine learning for conservation ................................................................................................................................. 17 Understanding the Relationship between Soundscape and Landscape Features in a Tropical Andean Environment ..................................................................................................................................................... 18 The Diversity of Heath Flowering Phenology– Revealing Fine Scale Patterns of Heterogeneity by High Resolution Drone Cameras ............................................................................................................................. 19 Using automated species identification in passive acoustic recording to test the acoustic niche partition hypothesis in Neotropical frogs ....................................................................................................................... 21 Automated recognition of people and identification of animal species in camera trap images ....................... 22 A method for automatic creation of a vegetation map using high-resolution aerial photographs of unmanned aerial vehicles .................................................................................................................................................. 23 Complimenting long-term bird monitoring observations with acoustic sensors and camera traps: best of both worlds............................................................................................................................................................... 24 Optimisation of video monitoring of fish for reef assessment and management............................................. 25 Annotating Species Trait Images with Absolute Size Information Using Mobile Devices ............................... 26 Session R2.1 ................................................................................................................................................... 27 Understanding Species Distribution, population Dynamics and Phenology by Machine Learning ................. 28 Performance evaluation and hyperparameter tuning of statistical and machine-learning models using spatial data .................................................................................................................................................................. 29 Statistically reinforced machine learning for nonlinear interactions of factors and hierarchically nested spatial patterns ............................................................................................................................................................ 30 Reconstruction and Recognition of Spatial Patterns from Sparse Data in the Problem of Biological Invasion ......................................................................................................................................................................... 31 Modeling Green Peach Aphid populations exposed to elicitors inducing plant resistance on peach ............. 32 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 ............................................................................................................................. 33 Community and Population Abundance Patterns in Benthic Macroinvertebrates in Streams Unravelled by Species Abundance Distribution and Machine Learning ................................................................................. 34 Page 3 of 299 th th 24 - 28 September 2018 https://icei2018.uni-jena.de Proceedings of the 10th International Conference on Ecological Informatics Modelling urban bird breeding sites with a random forest classifier using indicators of spatial heterogeneity in plant communities derived from earth observation data .................................................................................. 35 A machine learning approach to the assessment of the vulnerability of Posidonia oceanica meadows ........ 36 Supervised learning methods to predict species interactions based on traits and phylogeny ........................ 37 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 ............................................................. 38 Causal relationships of Cylindrospermopsis dynamics with water temperature and N/P-ratios: a meta- analysis across lakes with different climate based on inferential modelling by HEA ...................................... 39 Remote Sensing based Estimation of Forest Biophysical Variables using Machine Learning Algorithm ....... 40 A mixed model approach to modelling global habitat suitability and invasion risk of the American bullfrog ... 41 Dynamics of Four Cyanobacteria in the Nakdong River, South Korea over 24 years (1993-2016) Patternized by an Artificial Neural Network ........................................................................................................................ 42 Integrating context-based recommendation with deep CNN image classification for on-site plant species identification ..................................................................................................................................................... 43 Session S1.1 .................................................................................................................................................... 44 Earth Observation for ecosystem analysis and decision making