To address declining global biodiversity, large-scale and long-term standardized wildlife surveys allow biologists to directly compare data across time and space to identify drivers of species declines. However, standardized surveys are resource-intensive, often leading to significant data gaps. One source of abundant and inexpensive data are wildlife observations from the public (e.g., community/citizen science). When combined, community observations can provide wildlife data from areas that lack high-quality survey data, while standardized surveys can account for the opportunistic nature that makes community observations difficult to use in wildlife studies. Therefore, integrating high-quality survey data with abundant community observations may produce more accurate wildlife distribution models than models produced with either dataset alone. I aim to determine the efficacy of integrating standardized survey data with community-sourced observations to model the environmental factors that influence species distributions. I used data from Snapshot USA, a standardized nation-wide camera trap survey, and iNaturalist, an online platform for community members to upload wildlife observations, to compare red fox (Vulpes vulpes) distribution models created with both datasets. Preliminary results suggest that the Snapshot USA and iNaturalist datasets do produce differing red fox distribution models. Although analyses are ongoing, I expect to find that species distribution models created with integrated datasets will provide more accurate information on environmental factors influencing species distributions than models created with Snapshot USA or iNaturalist datasets alone. The framework I establish to integrate standardized wildlife surveys with community-generated data will be used to create more accurate distribution models for species of conservation concern.

Class Standing

Graduate Student



Faculty Advisor

Diana Lafferty

Faculty Advisor Email