Foraging and movement ecology in the airspace – Yuval Werber

My research encompasses the main animal groups that use the airspace and incorporates cutting edge technologies aimed at addressing major knowledge gaps related to the distribution and ecological interactions of aerial animals. My work includes continuous, year round data collection from radars and seasonal field work for tracking individual in their natural environment.

Research goals and main methods:

  • To study how the size and taxonomy aerial flyers affect their response to weather conditions (temperature and wind): The main methods in this project involve the collection of radar data from around the year and from several locations, and integration of this data with products of weather reanalysis models in order to scrutinize the effects of animal size and taxonomy on the distribution (including altitude), abundance and behavior of flying animals over more than seven orders of body masses (from 5 milligram insects to 12 kg white pelicans).
  • To study how movement and foraging behavior change with age in juvenile European bee-eaters (Merops apiaster): During three field seasons (April-September), I will fit 300 bee-eaters with ATLAS transmitters and examine how movement and behavior, specifically the birds’ response to harsh weather conditions, develops after fledging from their nest over a period of 3-4 months until they start their first migration to Africa. I am currently developing an automatic radar classifier for the species by integrating ATLAS and radar data and implementing machine learning techniques, which will sunstantially facilitate this work by increasing sample size of flying bee-eaters.
  • To study how foraging movement, behavior and success are affected by prey characteristics in the Greater mouse-tailed bats (Rhinopoma microphyllum): Three field seasons (August-September), fitting 60 Greater mouse tailed bats with Vesper accoustic and GPS tracking devices and analyze foraging success, movement and habitat choice with relation to insect morpholgy and distribution as recorded by KC-18XV entemological vertical looking radar located in the research area. Constructing automatic radar classifiers for the species by integrating accoustic monitoring and BirdScan MR1 Observation and implementing machine learning techniques, which will enable habitat scale monitoring of behavior.