Kefei Yan

Continental-scale analysis with the HAAG group comparing the mammal communities Snapshot USA camera traps actually record with the communities IUCN range maps predict: where the two agree, where they differ, and how much sampling a reliable estimate needs. Advised by Jenny McGuire and Steve Mussmann, with poster co-authors Neelima Pandey, John Hiedo and Marissa Douglas. Built a reproducible pipeline that merges 987,979 Snapshot USA wildlife records with IUCN range maps and COMBINE species-trait data into 713,319 analysis-ready records (surveys from 2019 to 2023, August to November each year) across 109 mammal species, 7,340 cameras and 262 arrays. Findings so far: pooling individual cameras into arrays more than doubled mean agreement between observed and expected communities, from 0.254 to 0.575, so the agreement a network reports depends heavily on the spatial scale at which it is read; limiting the analysis to species of at least 500 grams closed about 96% of the national richness gap, so most of the remaining error is detection sensitivity; five years of continuous survey raised mean similarity from 0.518 to 0.660 across 41 repeatedly surveyed arrays. A Random Forest trained on habitat, land-cover, development and road-distance covariates to predict agreement at each camera found that local site conditions, led by distance to the nearest road, then cropland and forest cover, matter most at the scale of individual cameras. Camera footprints and array unions are built in EPSG:5070 and compared with spatial joins, Jaccard similarity, species-accumulation curves and Chao–Jost coverage. Next: which species traits drive the disagreements between camera traps and range maps. Presented as the poster "Bridging Empirical and Modeled Biodiversity: Tracking Agreement between Camera Traps and Species Range Maps" at the Georgia Tech OMSCS Conference in May 2026.