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Abstract Linear barriers pose significant challenges for wildlife gene flow, impacting species persistence, adaptation, and evolution. While numerous studies have examined the effects of linear barriers (e.g., fences and roadways) on partitioning urban and non‐urban areas, understanding their influence on gene flow within cities remains limited. Here, we investigated the impact of linear barriers on coyote (Canis latrans) population structure in Seattle, Washington, where major barriers (i.e., interstate highways and bodies of water) divide the city into distinct quadrants. Just under 1000 scats were collected to obtain genetic data between January 2021 and December 2022, allowing us to identify 73 individual coyotes. Notably, private allele analysis underscored limited interbreeding among quadrants. When comparing one quadrant to each other, there were up to 16 private alleles within a single quadrant, representing nearly 22% of the population allelic diversity. Our analysis revealed weak isolation by distance, and despite being a highly mobile species, genetic structuring was apparent between quadrants even with extremely short geographic distance between individual coyotes, implying that Interstate 5 and the Ship Canal act as major barriers. This study uses coyotes as a model species for understanding urban gene flow and its consequences in cities, a crucial component for bolstering conservation of rarer species and developing wildlife friendly cities. 

Abstract For wildlife inhabiting snowy environments, snow properties such as onset date, depth, strength, and distribution can influence many aspects of ecology, including movement, community dynamics, energy expenditure, and forage accessibility. As a result, snow plays a considerable role in individual fitness and ultimately population dynamics, and its evaluation is, therefore, important for comprehensive understanding of ecosystem processes in regions experiencing snow. Such understanding, and particularly study of how wildlife–snow relationships may be changing, grows more urgent as winter processes become less predictable and often more extreme under global climate change. However, studying and monitoring wildlife–snow relationships continue to be challenging because characterizing snow, an inherently complex and constantly changing environmental feature, and identifying, accessing, and applying relevant snow information at appropriate spatial and temporal scales, often require a detailed understanding of physical snow science and technologies that typically lie outside the expertise of wildlife researchers and managers. We argue that thoroughly assessing the role of snow in wildlife ecology requires substantive collaboration between researchers with expertise in each of these two fields, leveraging the discipline‐specific knowledge brought by both wildlife and snow professionals. To facilitate this collaboration and encourage more effective exploration of wildlife–snow questions, we provide a five‐step protocol: (1) identify relevant snow property information; (2) specify spatial, temporal, and informational requirements; (3) build the necessary datasets; (4) implement quality control procedures; and (5) incorporate snow information into wildlife analyses. Additionally, we explore the types of snow information that can be used within this collaborative framework. We illustrate, in the context of two examples, field observations, remote‐sensing datasets, and four example modeling tools that simulate spatiotemporal snow property distributions and, in some cases, evolutions. For each type of snow data, we highlight the collaborative opportunities for wildlife and snow professionals when designing snow data collection efforts, processing snow remote sensing products, producing tailored snow datasets, and applying the resulting snow information in wildlife analyses. We seek to provide a clear path for wildlife professionals to address wildlife–snow questions and improve ecological inference by integrating the best available snow science through collaboration with snow professionals.