Pre-conference short courses
Pre-conference short courses
Saturday 16 September, 9:30-17:30 to Sunday 17 September, 9:30-16:00.
To register your attendance for one of the following short courses, please revisit your registration. Tickets are $100.00 USD.
Pre-conference short course 1
Title: Mathematical and computational approaches to spatial modelling in ecology
Organiser: Fugo Takasu, Nara Women's University, Japan
Outline: In this course, I present various approaches to model spatial population dynamics in ecology. Mathematical models of population dynamics describe how population size in focus changes with time. These are usually given as ordinary differential equations in continuous time or difference equations in discrete time that are mathematically tractable to a certain degree. These "non-spatial" models can be extended to be "spatial" where spatial distributions of populations are explicitly considered, e.g., lattice models in discrete space or reaction diffusion models in continuous space. Apart from these analytical approaches, spatial population dynamics can be algorithmically or computationally described as an individual-based model as a point pattern dynamics where a point as an individual is newly generated by birth from a parent, or deleted by death, or shifted by movement in continuous space. I present a general way to implement a point pattern dynamics as a simulation model. I discuss how analytical and algorithmic models are relevant to each other.
Pre-conference short course 2
Title: Population genetics theory and its application
Organisers: Professors Yong-Jin Won and Yuseob Kim, Ewha Womans University, South Korea
Outline: In this short course, lectures will be given on the essential concepts and mathematical models of population genetics for inferring population structure, demographic history, and natural selection from DNA sequence data. It is aimed for biologists who have basic knowledge in genetics and/or experience in empirical genetic data analysis but are not familiar with theoretical interpretation and quantitative modeling. Topics in the first half of the course include DNA sequence variation, coalescent theory, linkage and recombination, inference of complex demography, and inference of natural selection. In the second half, recent literature that uses these theoretical principles in elucidating the history and structure of natural populations will be reviewed. The primary aim of this review is to provide insights on what kinds of ecological questions can or cannot be answered from genomic data. At the end of the course, we will have a consulting time for genetic data brought by attendees who are interested in which ecological and evolutionary questions could be sought with the data, and furthermore which kinds of analyses are needed. If you are interested in the genetic consulting, we encourage you to contact us in plenty of time ahead before the workshop.
Pre-conference short course 3
Title: Practical use of machine learning in ecological modelling in R environment software
Organisers: Prof. Sovan Lek, University of Toulouse, France
Outline: The objective of this course is to explore the possibilities of R software with relevant packages to perform a variety of Ecological Models. Both Unsupervised and Supervised learning algorithms will be studied with the most up-to-date algorithms. In unsupervised learning, effort will focus on SOM and Fuzzy logic comparing to the more classical classification methods. In supervised learning, effort will focus on tree-based family models, SVM and ANN.
Pre-conference short course 4
Title: How biological organisms are digitalized for density estimation and morphological/behavioral detection for ecological research?
Organiser: Chunlei Xia, Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences, China
Outline: This course focuses on introducing basics of image analysis of biological organisms for ecological studies. This course aims at helping ecologist to improve the efficiency of utilizing image analysis tools in their works. Fundamental of image processing will be presented to understand the analysis of biological individual images. Practical applications of biological image processing will be given, such as detecting individual insects for estimating insect density, morphological/shape analysis of insects or fishes. Pattern recognition will be studied with its applications in plant leaf recognition and insect species classification. At last, I will present the principle of 2D/3D fish behavioral tracking systems with several examples.