Our research group defines structural ecology as a subset of spatial ecology that focuses on the dynamic interaction between individual organisms and the structure and pattern of their surroundings. We ask: What do they perceive? If they can move, how do they navigate? Find each other? Avoid risk? And ultimately, how do these translate into broader population and species-level dynamics?
We ask these questions because much of spatial ecology, such as biogeography and metapopulation theory, contains rather implausible assumptions about how individuals disperse in their environment. This makes predictions based on these theories suspect, even as they are the foundation for a lot of conservation activity.
We fit integrated step selection functions to animal movement data to build models of their response to their surrondings, including both traditional environmental features (vegetation, water, etc.) and social features (the location of nearby conspecifics). Over the years the lab has looked at data on elephants, bears, humpback whales, sheep, goats and baboons.
PhD student Grant Bowers is censusing the mammal communities on the coastal islands of Maine and showing that the different distribution limitations of different species lead to non-nested mammal communities not necessarily found on the mainland.
These are often in areas outside the core topics described above. If you are interested contact the lab PI, Dr. Gareth Russell, at russell@njit.edu.
DESCRIPTION: Review camera trap footage of white-tailed deer, scoring the deer for health based on a rubric. It is part of a project to asses the the stresses associated with living on islands rather than the mainland.
SUITABILITY: Undergraduates from almost any major. Could be an Independent Study.
EXPERIENCE REQUIRED: None. You will be trained on how to assess the deer.
DESCRIPTION: The idea is to take images of traditional urban plantings, such as those around campus, and use AI tools to re-render them with natural plants. These may be used in a future survey.
SUITABILITY: Undergraduates from CS or a related field, or anyone who has experience with AI image generation.
EXPERIENCE REQUIRED: An existing background in AI image tools, or at least a willingness to learn.
DESCRIPTION: Is it possible to achieve a stable world lock from a long-range landscape view? For example, if I am at the top of a mountain, with other peaks stretching out in front of me, can my AR device a) stably lock the virtual environment to the horizon, and b) combine GPS/compass info about where I am and which direction I am pointing with a known terrain map to figure out what those peaks are and thus also geo-reference the virtual world? This is a pre-requisite for an over-arching goal to enable a stable, hopefully immersive overlay of spatial data on real-world landscapes.
SUITABILITY: Undergraduates from CS or a related field who have experience with AR/MR/XR tools.
EXPERIENCE REQUIRED: A background in basic AR/MR/XR development, e.g., using Apple’s ARKit or Android ARCore, or at least a willingness to learn it on your own.