On March 30th, the seminar series will welcome two lightning talks from PhD students in our department, introducing their research. Litty Mathew will speak about and James O’Malley will speak about . The details for their talks are below.
James O’Malley
Abstract
The LegacyNet project is a network of 32 international sites, established to investigate the yield benefits of multispecies grassland leys and their legacy effects on a follow-on crop. A common experiment is being conducted at these sites, spread across 17 different countries, and the results aim to be relevant as a practical farm management strategy. This talk will begin by introducing the LegacyNet experiment, its design, and overall aims and objectives. It will also detail the current stage of the project and some of the diversity interactions (DI) modelling approaches that we plan to use to analyse the data.
Litty Mathew
Abstract
Obtaining reliable biodiversity data on the ground is expensive, requiring field surveys, sample collection and processing. Consequently, it tends to be done at coarse spatial and temporal resolutions. This hinders our understanding and capacity to predict the impacts of human disturbances on ecosystems. The emergence of passive monitoring acoustic, video and radar technologies for observing biodiversity resolve these problems as they produce high-resolution data. Such real-time data can be challenging to analyse and interpret as it can be highly variable, multivariate, zero-inflated and complex in structures over space and time. We are investigating the performances of different existing modelling options such as time series and hidden Markov models for describing the population level effects; for example, changes in species count over a period of time. We apply these methods to Japanese OKEON (Okinawa Environmental Observation Network) project data collected to monitor the dynamics of Okinawa’s terrestrial environments over time using modern monitoring technologies. The goal of our analysis is to extend the aforementioned approaches to identify the critical transitions in a real time monitoring system by modelling multiple processes acting at difference scales, and to link state-switching to space as well as time considering the heterogeneity in the data.