BIOlogical response RATES to current rates of environmental changes

Global Animal Species Richness

Throughout my PhD project which this is the parent project to, I aim to understand and predict the consequences of differential extinctions across trophic levels on a global scale. In doing so, I am focusing on drivers of differential extinctions, species-associations, extinction debt, biodiversity changes, shifts in functional communities, as well as ecosystem processes. These are my revised plans following my initial project ideas.

Co-Occurrences & Species-Interactions

Firstly, I collect(ed) species-occurrence records from a variety of data sources and aggregate(d) these to global and local co-occurrence matrices at the species-level. Subsequently, I use(d) additional data such as phylogenies and trait expressions of all species included in my analysis to delineate species-interactions and build global as well as local species-dependence networks. I then compare(d) these in terms of their informative quality and network topology to elucidate the pros and cons of using different data sources and approaches to delineate species-networks.

Climate Data & Extinction Risks

My PhD project leverage(s/d) state-of-the art climate reanalysis data which vastly improves on previously utilised legacy data sets of macroecology (see my KrigR package from my (KrigR - Downloading and Downscaling of ERA5(-Land) data using R porject for more information). I use(d) these spatio-temporal data sets to assess local and global risk of extinction for each species contained within my analysis by virtue of safety margins, range shifts/contractions, or IUCN red list status.

Extinction Debt Effects

Using the estimates of species extinctions, I perturb(ed) local and global species-dependence networks by removing vertices of species estimated to go extinct. This generates an extinction cascade (also known as extinction debt) which I analyse(d) according to two different approaches:

  • Within Trophic Levels – This step look(s/ed) at changes in local species richness and species-dependence network architecture for each trophic level individually (e.g. only considering plant networks).
  • Across Trophic Levels – These are/were assessed by quantifying the changes in cross-trophic species networks which aim to leverage as much data as possible while offering the highest spatial coverage possible.
Erik Kusch
Erik Kusch
PhD Student

In my research, I focus on statistical approaches to understanding complex processes and patterns in biology using a variety of data banks.

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