Building a mechanistic understanding of predation with GPS-based movement data

Evelyn Merrill, Håkan Sand, Barbara Zimmermann, Heather McPhee, Nathan Webb, Mark Hebblewhite, Petter Wabakken, Jacqueline L. Frair

Research output: Contribution to journalArticlepeer-review

86 Scopus citations


Quantifying kill rates and sources of variation in kill rates remains an important challenge in linking predators to their prey. We address current approaches to using global positioning system (GPS)based movement data for quantifying key predation components of large carnivores. We review approaches to identify kill sites from GPS movement data as a means to estimate kill rates and address advantages of using GPS-based data over past approaches. Despite considerable progress, modelling the probability that a cluster of GPS points is a kill site is no substitute for field visits, but can guide our field efforts. Once kill sites are identified, time spent at a kill site (handling time) and time between kills (killing time) can be determined. We show how statistical models can be used to investigate the influence of factors such as animal characteristics (e.g. age, sex, group size) and landscape features on either handling time or killing efficiency. If we know the prey densities along paths to a kill, we can quantify the 'attack success' parameter in functional response models directly. Problems remain in incorporating the behavioural complexity derived from GPS movement paths into functional response models, particularly in multi-prey systems, but we believe that exploring the details of GPS movement data has put us on the right path.

Original languageEnglish
Pages (from-to)2279-2288
Number of pages10
JournalPhilosophical Transactions of the Royal Society B: Biological Sciences
Issue number1550
StatePublished - Jul 27 2010


  • Carnivores
  • Functional response
  • Handling time
  • Kill rates
  • Movement
  • Predation


Dive into the research topics of 'Building a mechanistic understanding of predation with GPS-based movement data'. Together they form a unique fingerprint.

Cite this