Dr Diego Morales-Navarrete, CASCB & Zukunftskolleg, University of Konstanz
On modelling and estimating geo-referenced count spatial data with excessive zeros
Modelling spatial data is a challenging task in statistics. In many applications, the observed data can be modelled using Gaussian, skew-Gaussian, or even restricted random field models. However, in several fields, such as population genetics, epidemiology, and population dynamics, the data of interest are counts with excess of zeros in some cases, and therefore the mentioned models are not suitable for their analysis. Consequently, there is a need for spatial models that can adequately describe data coming from counting processes and handle the excess of zeros in data. Three approaches are commonly used to model this type of data, namely, GLMMs with Gaussian random field (GRF) effects, hierarchical models, and copula models. Unfortunately, these approaches do not explicitly characterize the random field like their q-dimensional distribution or correlation function. It is important to stress that GLMMs and hierarchical models induce a discontinuity in the path. Here, we propose a novel approach to efficiently and accurately model spatial count data with excess of zeros to deal with this. This approach is based on a random field characterization for count data with excess of zeros that inherit some well-known geometric properties from GFRs.
My name is Diego Morales-Navarrete. I am a mathematical engineer working on theoretical approaches for modelling spatio-temporal count data. I completed an undergraduate program in mathematical engineering at Escuela Politécnica Nacional in Quito-Ecuador. Then, I worked as a researcher and data scientist in public and private companies in Ecuador for a few years. In the beginning of 2022, I obtained a Ph.D. degree in Statistics at the Department of Statistics of Pontificia Universidad Católica de Chile. As a doctoral researcher I was working on developing novel approaches for modelling spatial and spatio-temporal count data. Nowadays, I am working as lecturer and researcher at Yachay Tech University in Ecuador.
Dr Yuqi Zou, CASCB & Zukunftskolleg, University of Konstanz
Social learning strategies in budgerigars’ foraging behaviours
Social learning occurs widely in various taxa. Although the costs associated with social learning are lower than that of individual learning based on personal information, social information is generally less reliable. Individuals need to consider which individuals they should learn from and to balance benefits and costs of different sources of information. I will present my PhD project that investigated the effects of age and number of tutors on social transmission of foraging information and the trade-off between personal and social information in budgerigars (Melopsittacus undulatus). My research shows that only female juveniles but not male juveniles were more likely to copy the choices of adult individuals. To test whether individuals prefer personal or social information, I compared the time spent at different coloured feeders before and after observing others foraging at the different feeders. This experiment demonstrated that individual switched their feeding preferences and spend more time feeding at less-preferred feeders, but the number of demonstrators had no significant effect on learning preference. These results suggest that social information is critical for initial learning decisions, but that budgerigars combine social and personal information to make foraging choices. Besides I will introduce the current project about collective mobbing behaviour and its fitness consequences.
Zou Yuqi graduated in January 2022 from the Institute of Zoology, Science Academy of China with a PhD in Ecology. During the Herz Fellowship, Zou Yuqi will focus on collective mobbing behaviour. The project will be carried out under the supervision of Dr. Michael Griesser and Prof. Iain Couzin.