Roads and highways act as barriers to wildlife. They disrupt movement of wildlife populations and connectivity between communities of interacting species. Transportation organizations and many wildlife agencies see highway crossing structures for wildlife as critical to mitigating highway barrier effects. These structures are optimistically assumed to be effective for most species, most of the time, but are seldom critically investigated.
Wildlife use of highway crossing structures can be highly variable and dependent on structural attributes, human use, and traffic conditions. Studies of animal behavior suggest that wildlife aversion to roadways—and possibly to crossing structures—could be related to traffic noise and light. If transportation organizations and wildlife agencies can confirm this effect they may be able to design more effective wildlife crossing structures and manage existing structures to increase their use by wildlife.
This policy brief discusses findings from research that measured traffic noise levels and used camera traps placed at 20 bridges and culverts in California that were known from previous work to pass at least one species.
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Shoreline habitats and infrastructure are currently being affected by sea level rise (SLR) and impacts will only worsen as global temperatures continue to rise. Decisions made by governments and individuals to adapt to SLR will have profound consequences for coastal ecosystems, transportation systems, and urban settings.
Federal guidance for adaptation relies on predictive models to guide planning. This includes planning for the recovery of endangered species in the face of SLR, which is mandated by the federal Endangered Species Act. FHWA and other federal organizations have recognized that new monitoring methods will be needed in order to collect new kinds of data and at a finer scale and wider extent. California among other states, provides extensive step-by-step guidance on how to plan for SLR, including the use of predictive models, and identifies the need for monitoring as well. Despite the recognized need for monitoring methods, no detailed guidance is given at the state level in California or federal level for how to do this.
Measurement of sea level has historically been achieved by using tide gauges and global satellite altimetry. There is no consistent method or system for measuring and recording shoreline change over large areas and at fine resolution other than infrequent and expensive LiDAR overflights that do not capture seasonal fluctuations. This policy brief summarizes findings from the project which utilizes a method to monitor shoreline and infrastructure changes in response to SLR using a network of time-lapse cameras.
As roads and other developed land uses proliferate, the resulting habitat fragmentation and loss of wildlife connectivity hinder animals’ ability to forage, establish new territories, and maintain genetic diversity. Wildlife crossing structures such as culverts and bridges theoretically can reduce these impacts by allowing species to effectively cross highways. However, previous research has shown that traffic presence and density can disrupt wildlife use of highway crossing structures, and that noise and light from human activities can affect animal behavior. Researchers at the University of California, Davis, Road Ecology Center measured traffic noise and light levels and placed motion- and heat-triggered cameras at 26 bridges and culverts along four interstate highways, 11 state highways and one major county road across California. The presence and behavior of animals at these highway crossing structures were compared to those detected at sites unaffected by roads to understand the effects of noise and light from a highway on wildlife behavior. This policy brief summarizes findings from that research and provides policy implications.
The United States Forest Service is required to analyze road systems on each of the national forests for potential environmental impacts. We have developed a novel and inexpensive way to do this using the Ecosystem Management Decision Support program (EMDS). We used EMDS to integrate a user-developed fuzzy logic knowledge base with a grid-based geographic information system to evaluate the degree of truth for assertions about a road’s environmental impact. Using spatial data for natural and human processes in the Tahoc National Forest (TNF, California, USA), we evaluated the assertion "the road has a high potential for impacting the environment." We found a high level of agreement between the products of this evaluation and ground observations of a TNF transportation engineer, as well as occurrences of road failures. We used the modeled potential environmental impact to negatively weight roads for a least-cost path network analysis to 1573 points of interest in the forest. The network analysis showed that out of 8233 km of road analyzed in the forest, 3483 km (42%) must be kept in a modified road network to ensure access to these points. We found that the modified network had improved patch characteristics, such as significantly fewer "cherry stem" roads intruding into patches, an improved area-weighted mean shape index, and larger mean patch sizes, as compared to the original network. This analysis system could be used by any public agency to analyze infrastructure for environmental or other risk and included in other mandated analyses such as risks to watersheds.
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