D06

Uncovering complex behavioural niche mechanisms from individual-level ecological time series

Roland Langrock

Ever more complex individual-level data collected using sensor technology allows ecologists to draw increasingly comprehensive pictures of behavioural niche mechanisms. State-switching models such as hidden Markov models (HMMs) are particularly suited to draw ecological inferences from various types of such time series data, e.g. GPS, video or acceleration data, as they allow not directly observable behavioural modes to be uncovered and related to individual-specific features as well as environmental covariates. In the second phase, we will:

1. develop new methodology for analysing behavioural time series data, which aims at i) formally distinguishing and investigating distinct behavioural phenotypes within a species, via mixtures of HMMs, and ii) inferring complex temporal niche mechanisms, including interactions with internal and external drivers as well as individual variation;
2. analyse data collected within the CRC, in particular the fur seal offspring data (pup production over time) collected within Project A01 (Hoffman & Gossmann), the mice lab data (movement between compartments of a cage) collected within Project A02 (Richter), the data related to the onset of metamorphosis in fire salamanders (capture-recapture) collected within Project A04 (Caspers), the Galápagos sea lion data (horizontal movement and diving) collected within Project B07 (Krüger), the beetle data (activity) collected within Project C01 (Kurtz), and the buzzard data (movement between habitats) collected within Project C03 (Chakarov & Krüger).

Collaborations

Established and planned collaborations within the CRC