Hi. I’m Erik Kusch, a PhD student of Ecoinformatics and Biodiversity who creates and applies novel statistical methods to big-data.
I aim to understand how global and local processes and patterns in biological systems come about and are reinforced thus generating knowledge about the resilience of the Earth’s ecosystems. My PhD project is under the guidance of Alejandro Ordonez and co-supervised by Roberto Salguero-Gómez.
Projects
A few things I am currently working on or have finished researching (for now).
Causes and Processes of Dryland Vegetation Memory
Throughout this project, I aim to identify underlying causes - biological and abiotic - to the striking patterns of vegetation memory I identified in a previous project.
Bayes Study Group
I have established and run a group-exercise for learning Bayesian Statistics from the ground up with an assortment of colleagues. This group was run through zoom and open to anyone.
BIOlogical response RATES to current rates of environmental changes
My PhD project is a part of this greater research framework within which my colleagues and I investigate how species compositions and interactions are shaped.
Effects of Biological Extinctions across Scales and Trophic Levels
My PhD project and its working packages.
KrigR - Downloading and Downscaling of ERA5(-Land) data using R
An R package designed for intuitive downloading, aggregating, and statistical downscaling of ERA5(-Land) data.
Vegetation Memory across Global Dryland Regions
Vegetation memory has been proposed as a proxy for ecosystem resilience. Here, I investigate how well this proxy captures processes of vegetation performance.
Publications in Review
A couple of publications I have submitted and am awaiting a publication decision on.
Recent Publications
Research I have published and that you can read and digest at your leisure.
Courses
Some teaching and course material I have created and feel is good enough to share.
An Introduction to Biostatistics
Lectures and seminars for B.Sc. students of biology which should serve as an introduction to the basics of biostatistics using R.
Bayesian Networks
Material for a Bayesian Network study group I am running.
BFTP - Biome Detection through Remote Sensing
Exercises for the BFTP (Biological Scientific Research in Theory and Practice) course at Aarhus University. Here I walk you through the basics of remote sensing analyses in R as needed for macroecological purposes.
Excursions into Biostatistics
A selection of talks related to general topics of biostatistical concern I have given throughout the years.
KrigR Workshop
Workshop material for getting started and becoming adept at using the R Package KrigR.
Statistical Rethinking
Summaries and solutions to exercises contained in the Statistical Rethinking material by Richard McElreath.
Recent Posts
Week 05-08 2021 | Aarhus, DK
[English] The Highs and Lows of PhD Life If you remember my last PhD-Diary entry, you will know just great of a start I had into 2021 in terms of PhD life and accomplishments.
Week 50-53 2020 & Week 01-04 2021 | Aarhus, DK
[English] New Year, Old Me, New Motivation Today is going to be a big one. Not only because I missed my last monthly PhD-Diary entry, but also because a lot happened in the last two months.
Week 45-49 2020 | Aarhus, DK
[English] Workshops Galore Can it really be? A new PhD Diary entry by me? Almost on-time as well?! You might be thinking: “Erik sure must have caught up with his workload to be able to write a Diary entry.
Week 38-44 2020 | Aarhus, DK
[English] Three monitors are not enough I was always told that the PhD life is pretty chill in the first year or so, but ramps up in terms of stress majorly towards the end of the third year.
Conference Talks & Presentations
Stuff I worked on by myself or with others and felt confident enough to put out there in front of people.
KrigR — A tool for downloading and statistically downscaling climate reanalysis data
KrigR package.
Climate Data Pipelines for the 21st Century - Efficient Data Retrieval and Processing
KrigR package.
Data Simplification for Ecological Network Inference
