Breakthrough at the Missouri River Breaks: A Quick Tool for Comparing Burned and Unburned Sites

A quantitative understanding of how forests work, both before and after (prescribed and wild) fire, is essential to management. Yet acquiring the kind of broad yet detailed information needed for many management decisions can be costly, tedious, and time-consuming. After two sweeping wildfi res in the Missouri River Breaks area of eastern Montana—the Indian and Germaine wildfi res—some researchers wanted to see whether it was possible to characterize both pre-fi re and post-fi re characteristics in a relatively inexpensive and effi cient way. Specifi cally, they wanted to know whether prescribed fire that is then followed by wildfire, is more likely to meet management objectives. Theresa Jain, a research forester at the Forest Service, Rocky Mountain Research Station, and her colleagues set out to do just that. After creating a careful plan, a small crew set off into the area, collected quick, but thorough data, and photographs. They were able to compare “pre burn” (untouched by fi re) areas, to areas that had been exposed to wildfire, prescribed fire, or both. They created summaries and handbooks for their results. Although the data are not statistically signifi cant, there is a trend in the region of this study suggesting that wildfi re after a prescribed burned is more effective at meeting management objectives than either wildfi re or prescribe fi re alone. The handbooks offer not only specific information on the region, but also serve as a handbook for managers and planners who want to do the same thing in a different region

Restoring Mountain Meadows: Using Fire, Vegetation, and Fuel Management in Western Oregon

Meadows occupy a small percentage of the western Cascade landscape. Yet they sustain an abundance of species that do not exist in adjacent forests. These biologically rich habitats have been shrinking for more than a century as a result of conifer encroachment. Charlie Halpern, at the University of Washington, and his colleagues combined retrospective and experimental research to understand the consequences of encroachment for these ecosystems, and whether, and under what conditions, it was possible to restore meadows through tree removal and prescribed burning. Their initial results indicate that meadow species are replaced by forest herbs within decades of tree establishment and that early intervention may greatly aid restoration. However, they also found that tree removal, with or without burning, benefi ts meadow species at the expense of forest herbs, suggesting strong potential for restoration where meadow species still persist

Mapping and Estimating Forest Fuel with Radar Remote Sensing

With an increase in the risk of large fires across much of the Western United States, along with a growing variety of fuel types that result from changes in the landscape and management strategies, there has never been a more pressing need for accurate, cost-efficient, large scale forest fuel maps. Emerging remote sensing technologies may yield exactly the kind of large scale maps needed to more accurately predict forest fuel loads, fire risk, and fire behavior. With the Greater Yellowstone Ecosystem as their backdrop, Don Despain, Sasaan Saatchi, Kerry Halligan, Richard Aspinall, and Robert Crabtree worked together to acquire a detailed catalogue of remote sensing data for estimating forest fuel load, and creating subsequent maps. They retrieved passive (optical) and active (radar and LiDar) remote sensing data from a variety of sensors, interpreted the data, combined the data, and created maps—all with the intent of fi nding the most accurate remote sensing data in terms of its correlation with their “on the ground” field data. They found remarkably close accuracy with their airplane-retrieved radar data, showing that particular sensors could achieve about 70 percent accuracy compared to field data in predicting fuel load. This work helps mark a new era of potentially more accurate and cost-effective remote sensing technology specifi cally in regards to estimating forest fuel load, and related mapmaking

Burning and Beetles: Why Does Fire Spark Bark Beetle Attack?

Prescribed burning is now a routine technique used in forests. In some cases, these forests have not experienced fi re for decades. Sometimes, prescribed fire can lead to unexpected consequences. In Crater Lake National Park, prescribed burning to restore the mixed conifer forest there began in the late 1970s with unexpected consequences. Eventually researchers, including Jim Agee, determined that bark beetles were inflicting tree damage, and death. Agee’s doctoral student, Dan Perrakis, focused his entire dissertation on trying to understand much more about the connections between fi re, trees, and bark beetles. With Agee, he did a host of interdisciplinary experiments. He found that at Crater Lake resin flow does not protect trees from beetles. It may be that beetles use resin volatiles released by fire-exposed trees, to home in on weakened trees. Says Perrakis, “The major take home point with this is that the beetles and trees are engaged in an evolutionary arms race,” Perrakis says. “But at Crater Lake, for now, the beetles are winning.” With this, there may be emerging guidance on how managers and planners can better protect forests from the ravages of bark beetles

Breakthrough at the Missouri River Breaks: A Quick Tool for Comparing Burned and Unburned Sites

A quantitative understanding of how forests work, both before and after (prescribed and wild) fire, is essential to management. Yet acquiring the kind of broad yet detailed information needed for many management decisions can be costly, tedious, and time-consuming. After two sweeping wildfi res in the Missouri River Breaks area of eastern Montana—the Indian and Germaine wildfi res—some researchers wanted to see whether it was possible to characterize both pre-fi re and post-fi re characteristics in a relatively inexpensive and effi cient way. Specifi cally, they wanted to know whether prescribed fire that is then followed by wildfire, is more likely to meet management objectives. Theresa Jain, a research forester at the Forest Service, Rocky Mountain Research Station, and her colleagues set out to do just that. After creating a careful plan, a small crew set off into the area, collected quick, but thorough data, and photographs. They were able to compare “pre burn” (untouched by fi re) areas, to areas that had been exposed to wildfire, prescribed fire, or both. They created summaries and handbooks for their results. Although the data are not statistically signifi cant, there is a trend in the region of this study suggesting that wildfi re after a prescribed burned is more effective at meeting management objectives than either wildfi re or prescribe fi re alone. The handbooks offer not only specific information on the region, but also serve as a handbook for managers and planners who want to do the same thing in a different region

Improving Access to Psychological Therapy: Initial Evaluation of the Two Demonstration Sites

The Government's Improving Access to Psychological Therapy (IAPT) programme aims to implement NICE Guidance for people with depression and anxiety disorders. In the first phase of the programme, two demonstration sites were established in Doncaster and Newham with funding to provide increased availability of cognitive-behaviour therapy-based (CBT) services to those in the community who need them. The services opened in late summer 2006. This paper documents the achievements of the sites up to September 2007 (roughly their first year of operation) and makes recommendations for the future roll out of IAPT services.Cognitive Behavioural Therapy, CBT, Psychological therapy, Evaluation, Cost benefit analysis, IAPT

Controlling the Precision-Recall Tradeoff in Differential Dependency Network Analysis

Graphical models have gained a lot of attention recently as a tool for learning and representing dependencies among variables in multivariate data. Often, domain scientists are looking specifically for differences among the dependency networks of different conditions or populations (e.g. differences between regulatory networks of different species, or differences between dependency networks of diseased versus healthy populations). The standard method for finding these differences is to learn the dependency networks for each condition independently and compare them. We show that this approach is prone to high false discovery rates (low precision) that can render the analysis useless. We then show that by imposing a bias towards learning similar dependency networks for each condition the false discovery rates can be reduced to acceptable levels, at the cost of finding a reduced number of differences. Algorithms developed in the transfer learning literature can be used to vary the strength of the imposed similarity bias and provide a natural mechanism to smoothly adjust this differential precision-recall tradeoff to cater to the requirements of the analysis conducted. We present real case studies (oncological and neurological) where domain experts use the proposed technique to extract useful differential networks that shed light on the biological processes involved in cancer and brain function