Submitted by Arthur Middleton on
Camera traps are a valuable tool for wildlife ecologists to be able to remotely collect data on the distribution and behavior of wildlife species. Students will assist with camera trap data processing and analysis for one of two projects:
1) Carnivore Community Response to a Novel Prey, the Magellanic Penguin: Rewilding efforts across the world are often predicated on restoring predator-prey relationships, which in theory will result in various ecological benefits. In Patagonia, rewilding has led to the increase of herbivores like guanacos and rhea, and their predator, the puma. However, along the Patagonia coast, pumas have switched to predating penguins. For this project, we are interested in using camera trap data already collected to estimate how the penguin colony is shaping the movement of pumas and other carnivores in Monte León National Park. Students will primarily classify and identify species in camera trap photos from our array in the park.
2) Prey Behavioral Responses to Prescribed Burning: Prescribed burning is often used as a form of habitat management in South Africa (and elsewhere) to promote the re-growth of vegetation. Although this new vegetation is highly nutritious for herbivores, recently-burned areas can also be risky for them because of the increased presence of predators. We are interested in using previously-collected camera trap data to better understand if prey alter anti-predator behaviors in response to prescribed burning. Students will identify behaviors of several common South African herbivores in camera trap photos.
Applicants should indicate in their Personal Statement which of the two projects they are interested in.
The students will be responsible for identifying animal species or behaviors in photos taken by remote camera traps. For self-motivated students, there may be additional opportunities to use the resulting data for subsequent analyses using Program R.
Looking for hardworking and self-motivated students with interests in wildlife ecology and statistical analysis. Prior experience with Program R (or other programming languages), statistics, and data analysis is preferred but not required.