21st Century Planning Techniques for Creating Fire-Resilient Forests in the American West

Data-driven decision making is the key to providing effective and efficient wildfire protection and sustainable use of natural resources. Due to the complexity of natural systems, management decision(s) require clear justification based on substantial amounts of information that are both accurate and precise at various spatial scales. To build information and incorporate it into decision making, new analytical frameworks are required that incorporate innovative computational, spatial, statistical, and machine-learning concepts with field data and expert knowledge in a manner that is easily digestible by natural resource managers and practitioners. We prototyped such an approach using function modeling and batch processing to describe wildfire risk and the condition and costs associated with implementing multiple prescriptions for risk mitigation in the Blue Mountains of Oregon, USA. Three key aspects of our approach included: (1) spatially quantifying existing fuel conditions using field plots and Sentinel 2 remotely sensed imagery; (2) spatially defining the desired future conditions with regards to fuel objectives; and (3) developing a cost/revenue assessment (CRA). Each of these components resulted in spatially explicit surfaces describing fuels, treatments, wildfire risk, costs of implementation, projected revenues associated with the removal of tree volume and biomass, and associated estimates of model error. From those spatially explicit surfaces, practitioners gain unique insights into tradeoffs among various described prescriptions and can further weigh those tradeoffs against financial and logistical constraints. These types of datasets, procedures, and comparisons provide managers with the information needed to identify, optimize, and justify prescriptions across the landscape.

Do Review Papers on Bird–Vegetation Relationships Provide Actionable Information to Forest Managers in the Eastern United States?

Forest management planning requires the specification of measurable objectives as desired future conditions at spatial extents ranging from stands to landscapes and temporal extents ranging from a single growing season to several centuries. Effective implementation of forest management requires understanding current conditions and constraints well enough to apply the appropriate silvicultural strategies to produce desired future conditions, often for multiple objectives, at varying spatial and temporal extents. We administered an online survey to forest managers in the eastern US to better understand how wildlife scientists could best provide information to help meet wildlife-related habitat objectives. We then examined more than 1000 review papers on bird–vegetation relationships in the eastern US compiled during a systematic review of the primary literature to see how well this evidence-base meets the information needs of forest managers. We identified two main areas where wildlife scientists could increase the relevance and applicability of their research. First, forest managers want descriptions of wildlife species–vegetation relationships using the operational metrics of forest management (forest type, tree species composition, basal area, tree density, stocking rates, etc.) summarized at the operational spatial units of forest management (stands, compartments, and forests). Second, forest managers want information about how to provide wildlife habitats for many different species with varied habitat needs across temporal extents related to the ecological processes of succession after harvest or natural disturbance (1–2 decades) or even longer periods of stand development. We provide examples of review papers that meet these information needs of forest managers and topic-specific bibliographies of additional review papers that may contain actionable information for foresters who wish to meet wildlife management objectives. We suggest that wildlife scientists become more familiar with the extensive grey literature on forest bird–vegetation relationships and forest management that is available in natural resource management agency reports. We also suggest that wildlife scientists could reconsider everything from the questions they ask, the metrics they report on, and the way they allocate samples in time and space, to provide more relevant and actionable information to forest managers.

Integrating Knowledge for Simulating Vegetation Change at Landscape Scales

Managers of public lands are increasingly faced with making planning decisions for dynamic landscapes with conflicting objectives. A modeling system has been designed to serve as a decision support system to help managers and resource specialists integrate the available knowledge of vegetation change and disturbance processes, and quantify concepts that are often difficult to interpret for specific landscapes. The system is named SIMPPLLE, an acronym taken from “<bi>SIM</bi>ulating vegetation <bi>P</bi>atterns and <bi>P</bi>rocesses at Landscape scaLEs.” SIMPPLLE can be used to help define and evaluate future conditions at landscape scales, to identify areas that are more prone to disturbances over a given time frame, to identify the options for influencing these disturbance processes, and to help design and evaluate different strategies for achieving desired future conditions. The emphasis in this article is to give an overview of the design of the system, the types of knowledge integrated, and the type of output produced. The initial validation work discussed indicates that the approach used for capturing and integrating process knowledge in SIMPPLLE does predict realistic results at landscape scales. SIMPPLLE provides managers a tool to integrate and interpret concepts of desired future conditions, range of variability, and the interaction between vegetation patterns and disturbance processes. SIMPPLLE provides a way to help evaluate proposed management scenarios within a future that includes stochastic processes. West. J. Appl. For. 19(2):102–108.