Despite previous assumptions that cities are a “lost cause” for biodiversity conservation, recent findings emphasize that urban ecosystems have tremendous ecological and conservation value. An emergent, unifying thread from recent research has centered on the importance of social-ecological dynamics and how such processes fundamentally shape the biology of cities. For instance, socioeconomic wealth is often positively associated with urban species richness and biodiversity, as well as ecological complexity. In addition, anthropogenic food subsidies dysregulate species interactions (e.g., predator-prey dynamics) with pervasive consequences for organismal health. Further, the heterogeneous distribution of human-associated contaminants and toxins, principally driven by residential segregation and gentrification, create a variable landscape of health that can jeopardize wildlife fitness within certain urban habitats. Hence, to build strategies that minimize species loss and promote long-term resiliency, it is essential to identify and quantify the relative contributions of human activity and the built environment to shaping urban biodiversity.
This faculty-initiated proposal will delve into how the social components of the greater metropolitan Bay Area – specifically environmental health disparities, housing insecurity, gentrification, and social attitudes towards wildlife – are associated with population and community-level dynamics of urban mammals. To do this, we will use motion-triggered camera traps to noninvasively survey the spatial and temporal patterns of Bay Area wildlife. Using both still images and video recordings, we will then code those data into a presence-absence matrix to calculate occupancy, detection, and colonization rates across field sites. In addition, we plan to incorporate unique GIS layers that quantify how ecological parameters of urban mammals correspond with societal features. In sum, these data will be valuable in assessing how wildlife respond to and interact with built environments, as well as provide insight into how wildlife behavior and ecology change in human-dominated landscapes.
Undergraduates will be critical partners in the field and data analysis components of this project. The components of their responsibilities include the following elements of the project:
- Field work and camera deployment: There is a substantial field component to this project, in which motion-triggered camera traps will be deployed throughout the East Bay and SF (n = 80+ camera stations) from January to July 2021. Deploying and monitoring those cameras will provide valuable field experience for undergraduates on the project, as well as a means of community engagement with the public around wildlife science and urban systems.
- Species photo identification: SPUR scholars will have the opportunity to learn how to identify species from photos collected from the field. Knowing how to accurately and effectively identify species in still images is a basic yet critical skill for rising and emerging scholars in wildlife sciences, and will help SPUR students both in this project and in any future projects that use camera traps as the main data collection method.
- Community engagement: Undergraduates will have opportunities to engage with many of our local partners – including the Oakland Zoo, City of Oakland, City of San Francisco, Cal Academy of Sciences, and the Presidio Trust – that are collaborating on and affiliated with the project.
- Data analysis in R: taking still images and translating them to actionable data is a necessary skillset for wildlife biologists. Hence, this project will intentionally have SPUR scholars run basic occupancy and detection models to determine how landscape features influence urban wildlife ecology. This will also give the scholars an introductory or additional experience to data analysis and interpretation in R.
Applicants should be interested in wildlife ecology and community-level dynanmics, as well as the social aspects of wildlife conservation. Applicants should have an excitement to learn about multidisciplinary methods. Required skills: Applicants should have some experience with the basics of ArcGIS Pro/ArcMap or another GIS software, competence in Microsoft Excel, an ability to work independently, an eye for detail, and patience with some repetitive tasks. Desirable additional skills (though not required): experience with remote sensing, experience with R.