Citizen Science and Its Contribution to the Conservation of Sacramento Valley Red Foxes in California

Red foxes found in the Sacramento Valley of California, referred to as SV red foxes throughout this thesis, were assumed to be of non-native ancestry until recent genetic testing confirmed their historical link to the area. The development of an effective conservation strategy for SV red foxes is currently underway, but was initially hindered by the lack of details regarding habitat requirements, occupancy patterns, and population sizes. In 2007, the University of California, Davis launched a fox reporting website for the public to document any fox sightings. Researchers actively pursued the public fox sighting reports from 2007-2009 and found 51 active red fox dens, 48 of which were native SV red fox dens, throughout the Sacramento Valley based on the locations provided in the reports.  The objectives of my research are to answer the following questions: (1) What can the data provided in the previously obtained fox sighting reports reveal about the website trends and fox ecology? (2) What types of relationships occur between the public and the SV red fox research efforts, and how can the participants’ experience be improved to ensure long-term participation? (3) Can a recently developed presence-only SV habitat model be used as a filter for future incoming reports, and what are the relationships between the reports with a high probability of fox presence and its associate report components?  In chapter one, I assess how effective the public sighting reports (n= 248) were in: assisting researchers with locating SV red fox dens, providing information about habitat preferences of the SV red foxes, and supplying insights of the website. The results showed that the distance from Yolo County, where the highest amount of SV red fox dens is located and where the initial study was conducted, can explain 38% of the variation in the quantity of reports collected from the website.  In chapter two, I administered an online survey to the residents of the Sacramento Valley to gauge their outlook on SV red foxes and their opinions of the fox reporting website. The survey respondents included both previous website visitors and new website visitors. I summarized the responses (n= 310 total, n= 210 complete) and ran a series of statistical analyses to evaluate the social characteristics of the participants utilizing the website. Overall, the majority of participants responded positively to the website, declaring it interesting and helpful. However, there is a low rate of return visitors. The main users of the fox sighting website are older participants and/or participants with a higher level of education.  In the third chapter of this research, I overlaid the fox sighting report locations onto the SV habitat model using ERSI® ArcMap v 10.0 and coded them for habitat suitability values. Analysing previously obtained fox sighting reports provided the necessary insight to define the criteria for the data filter. By implementing a habitat model filter at moderate habitat suitability levels, researchers can reduce the time it takes them to validate the public data received. However, a coinciding filter recording distribution frequency for report locations should also be implemented to compensate for dens located in low habitat suitability levels.  Prior to this research, the data collected from the public had never been evaluated for purposes other than locating potential SV red foxes. By reviewing feedback from the participants, I was able to characterise the participants using the fox reporting website and assess their general SV red fox knowledge. The findings of my research also lead to insights about SV red fox ecology. However, the major finding in this research shows that the majority of local citizens participating in this study indicated awareness of SV red foxes nativity and endangerment. The methods used in my research can be applied to other citizen science projects, to improve the overall efficiency and effectiveness of collecting data.