Integration of multiple, heterogeneous sensors is a challenging problem across a range of applications. Prominent among these are multi-target tracking, where one must combine observations from different sensor types in a meaningful and efficient way to track multiple targets. Because different sensors have differing error models, we seek a theoretically justified quantification of the agreement among ensembles of sensors, both overall for a sensor collection, and also at a fine-grained level specifying pairwise and multi-way interactions among sensors. We demonstrate that the theory of mathematical sheaves provides a unified answer to this need, supporting both quantitative and qualitative data. Furthermore, the theory provides algorithms to globalize data across the network of deployed sensors, and to diagnose issues when the data do not globalize cleanly. We demonstrate and illustrate the utility of sheaf-based tracking models based on experimental data of a wild population of black bears in Asheville, North Carolina. A measurement model involving four sensors deployed among the bears and the team of scientists charged with tracking their location is deployed. This provides a sheaf-based integration model which is small enough to fully interpret, but of sufficient complexity to demonstrate the sheaf’s ability to recover a holistic picture of the locations and behaviors of both individual bears and the bear-human tracking system. A statistical approach was developed in parallel for comparison, a dynamic linear model which was estimated using a Kalman filter. This approach also recovered bear and human locations and sensor accuracies. When the observations are normalized into a common coordinate system, the structure of the dynamic linear observation model recapitulates the structure of the sheaf model, demonstrating the canonicity of the sheaf-based approach. However, when the observations are not so normalized, the sheaf model still remains valid.

Human-dominated landscapes contain fragmented natural land cover interspersed throughout an urban matrix. Animals that occupy human-dominated landscapes often grow and reproduce differently than conspecifics. Female American black bears (Ursus americanus) produce litters for the first time usually at age 4 years; 2-year-olds rarely give birth. We visited winter bear dens and trapped bears in spring and summer to compare the reproductive output and weight of female black bears within the city limits of Asheville, North Carolina, and three forested rural sites in North Carolina and Virginia representative of the undeveloped habitat of Asheville. Urban yearling females weighed nearly double (45.0 kg ± 8.1 [± SD]; n = 36) that of yearling females from the three rural study sites (23.2 ± 8.5 [Pisgah], 23.6 ± 8.3 [Virginia SW], and 23.9 ± 9.7 [Virginia NW]; n = 95). Across all sites, hard mast production during the autumn, when females were cubs, did not affect their weights as yearlings. Seven of 12 (58%) 2-year-old urban bears produced 11 cubs (mean litter size = 1.6 ± 0.8), but no 2-year-old rural females produced cubs. Production of hard mast in the autumn, when females were yearlings, did not influence cub production by 2-year-old female bears at the urban site. We hypothesize that reproduction by 2-year-old bears is linked to the availability of anthropogenic food sources associated with urban environments. To inform population level management decisions, managers and researchers should quantify urban food sources and the effects on black bear life history. If high fecundity allows urban populations to sustain relatively high mortality rates, then urban bear populations may be source populations for surrounding, rural areas. Alternately, if reproduction in urban populations cannot match high time-specific or age-specific urban mortality rates, then urban populations may be sinks for the surrounding areas.

Wildfire occurrences have increased and are projected to continue increasing globally. Strategic, evidence-based planning with diverse stakeholders, making use of diverse ecological and social data, is crucial for confronting and mitigating the associated risks. Prescribed fire, when planned and executed carefully, is a key management tool in this effort. Assessing where prescribed fire can be a particularly effective forest management tool can help prioritize efforts, reduce wildfire risk, and support fire-resilient lands and communities. We collaborated with expert stakeholders to develop a Bayesian network model that integrated a large variety of biophysical, socioecological, and socioeconomic spatial information for the Southeastern United States to quantify where risk is high and where prescribed fire would be efficient in mitigating risk. The model first estimated wildfire risk based on landscape-scale interactions among the likelihoods of fire occurrence and severity and the people and resources potentially exposed—accounting for socioeconomic vulnerabilities as well as key ecosystem services. The model then quantified the potential for risk reduction through prescribed fire, given the existing fuel load, climate, and other landscape conditions. The resulting expected risk estimates show high risk concentrated in the coastal plain and interior highland subregions of the Southern US, but there was considerable variation among risks to different ecosystem services and populations, including potential exposure to smoke emissions. The capacity to reduce risk through fuel reductions was spatially correlated with risk; where these diverged, the difference was largely explained by fuel load. We suggest that both risk and the capacity for risk reduction are important in identifying priorities for management interventions. The model serves as a decision support tool for stakeholders to coordinate large-landscape adaptive management initiatives in the Southern US. The model is flexible with regard to both empirical and expert-driven parameterizations and can be updated as new knowledge and data emerge. The resulting spatial information can help connect active management options to forest management goals and make management more efficient through targeted investments in priority landscapes.

Island foxes ( Urocyon littoralis (Baird, 1858)) have experienced severe reductions in populations on 4 out of 6 California Channel Islands. Although numbers of foxes on San Clemente Island (SCLI; Urocyon littoralis clementae Merriam, 1903) have remained relatively stable, data on reproductive success in urban and rural areas are necessary to better understand population dynamics of foxes and manage the population if it sustains declines. Determining locations and characteristics of den sites will also assist in minimizing possible impacts of humans on foxes. We found that 10 female foxes produced at least 19 kits during 2008. Female foxes that spent a greater proportion of time within an urban area had greater probability of reproducing compared with foxes that infrequently utilized urban areas. However, these “urban” foxes often selected den sites nearer to roads, which may expose them to increased mortalities. We located 23 den sites on a mean of 17°–18° slopes with 40% having westerly facing aspects. We recommend avoiding human disturbance to these areas from February through June, which may minimize potential impacts on foxes. Our study should provide insight into managing island foxes, especially in respect to urban areas on other Channel Islands.

American black bear (Ursus americanus) populations in North Carolina, US have recovered significantly in recent decades and now occupy much of western North Carolina, including urban-suburban areas. We used the black bear as a potential sentinel for leptospirosis, a bacterial zoonotic disease caused by Leptospira spp., which is maintained by domestic and wild mammals. We determined whether Leptospira spp. were present across a gradient of housing densities in the urban and suburban black bear population in and around Asheville, North Carolina using serologic and molecular surveys. We collected blood from captured black bears (n=94) and kidneys and bladders from carcasses (n=19). We tested a total of 96 (47 females, 47 males, and 2 unknown) serum samples by microscopic agglutination test (MAT) and had positive results (titer >1:100) for L. kirschneri serovar Grippotyphosa (L. Grippotyphosa) in 4 females (8%) and 5 males (10%). No other serovars showed elevated titers in MAT. We tested a total of 125 samples using PCR (n=96 serum, n=20 kidney, and n=9 bladders) and obtained positive results from one serum (1%), one kidney (5%), and one bladder (11%). The presence of Leptospira spp. in black bears occupying an urban and suburban landscape may indicate a more extensive occurrence of the bacteria among animals in the study region because black bears are the top carnivore in that ecosystem. Potential threats of widespread contamination during natural events such as flood or drought must be considered.

Risks associated with severe wildfire are growing in forest landscapes due to interactions among climate change, fuel accumulation from fire suppression, an expanding wildland–urban interface, and additional factors. People, infrastructure, ecosystem services, and forest health all face varying degrees of risk. The spatial distributions of the many social and ecological factors that influence wildfire, its impacts, and management responses are an important landscape-level context for managing risks and fostering resilient lands and communities. Decision-support tools that integrate these varied distributions can provide a holistic and readily interpreted characterization of landscapes, helping fire management decision making be appropriate, efficient, and effective. Firescapes—landscape types defined in relation to fire, its drivers, and its effects as a socioecological system—fill this role, providing a way to organize and interpret spatial variation along multiple relevant dimensions. We describe a quantitative approach for classifying and mapping firescapes for decision support, using the southeastern United States as a case study. We worked with regional partners to compile relevant large-scale datasets and identify 73 variables for analysis. We used factor analysis to reduce the data to eight factors with intuitive interpretations relevant to fire dynamics, fire history, forest characteristics, climate, conservation and ecosystem service values, social and ecological landscape properties, and social vulnerabilities. We then used cluster analysis on the factors to generate quantitative landscape classes, which we interpreted as nine distinctive firescape classes. The firescapes provide a broad-scale socioecological information context for wildfire risk management and planning. The analytical approach can accommodate different data types at a variety of scales, incorporate new monitoring data as they are available, and can be used under data-driven scenarios to assess possible consequences of future change. The resulting firescape maps can provide decision support to forest managers, planners, and other stakeholders, informing appropriate strategies to manage fire and associated risks, build community and forest resilience to fire, and improve conservation outcomes.