Conservation Biology Institute is supporting the Spatial Informatics Group – Natural Assets Laboratory (SIG-NAL) on a multi-year Regional Wildlife Mitigation Program (RWMP) in Santa Barbara county that is funded by the National Fish and Wildlife Foundation. Specifically, CBI in partnership with SIG-NAL will develop and propose a fire-resistant buffer or “greenbelt” area in strategic locations within the program area to create wildfire resilient green space, working lands, and habitats. Program outcomes will also provide numerous co-benefits that support watershed and coastal ecological health using a suite of tools including:

The RWMP is designed to assess hazard, exposure and vulnerability and equitably reduce wildfire hazard across the Santa Barbara front country. The program goals are to decrease the risk of wildfire impacts to structures and infrastructure, promote wildfire resilient green space, working lands, and habitats, and develop community capacity to adapt and recover from the shocks of natural disasters. The Program is divided into three primary Resilience Domains: the Landscape Resilience Domain, the Built Environment Resilience Domain, and the Community Resilient Domain. Each domain will work collaboratively to foster resilience and build adaptive capacity that will allow the community to prepare, respond and recover from the shock of large wildfires. More details can be found at this link: https://rwmpsantabarbara.org/.

CBI will develop and apply a forest management decision-support system (DSS) for forest resilience planning in the southern Sierra Nevada that integrates the latest science on how vegetation, terrain, climate, and weather interact to influence fire risks and forest resilience. The interdisciplinary team led by CBI includes ecological modelers, forest ecologists, fire scientists, physicists, and statisticians. The core of the DSS will be a Forest Resilience Model built using EEMS (Ecosystem Evaluation Modeling System; Sheehan and Gough 2016). The DSS will be tested, refined, and applied to resilience planning in that portion of the modeling region of greatest concern to the interagency Sequoia Regional Partnership, which is working to restore ecologically resilient conditions in and near Sequoia National Forest and Sequoia-Kings Canyon National Park.

The resilience model evaluates forest resilience to fire, drought, and other factors based on landscape conditions. The DSS will allow managers to simulate fuel-reduction treatments, evaluate their effects on a range of risks and resources (e.g., fire, sequoias, fisher habitat), project the impacts into the future, and assess levels of uncertainty.  The DSS and component models will help managers understand how, in concert with terrain and weather, vegetation structure influences fire behavior and forest resilience. Importantly, the DSS will for the first time consider how fire-atmosphere coupling affects fire in models to support forest planning. This will apply how vegetation structure influences fire via both fuel arrangements and air flows, and thus more accurately reflect the full picture of how vegetation treatments may affect fire and fire effects on the landscape.

The DSS will be further refined and applied to resilience planning by the Sequoia Regional Partnership, whose primary focus is reducing fire risks to giant sequoia groves, fishers, and human communities.  

External Team members include: Joe Werne (NorthWest Research Associates NWRA), Christopher Wikle (Department of Statistics, University of Missouri) and David Marvin (SALO Science).   

Map of project study area.

The Forest Treatment Planner was developed to provide forest managers a platform for exploring the potential consequences of different forest management alternatives in both the short and long-term, examine the resource-based trade-offs inherent in any proposed vegetation management action, and clearly substantiate the rationale behind management planning. Originally envisioned as a means to help balance fisher habitat conservation with fuel reduction efforts, the Treatment Planner provides a dynamic link between GIS, the Forest Vegetation Simulator (FVS) modeling software, and any resource model (e.g. habitat, hydrology, fuel, economic) that uses the EEMS (Environmental Evaluation Modeling System) modeling environment. As such, the Treatment Planner is not a model per-se, but a system of communication between existing software that, when used together, can facilitate spatially-explicit comparisons and project refinement. By exporting an FVS output directly into the EEMS modeling environment, this framework allows for a transparent evaluation of the impacts to multiple resource values and a straightforward process for communicating these impacts to stakeholders.

The Treatment Planner supports an iterative process of treatment project simulation, adaptive management, and outcomes analysis, the steps in what we refer to as the “4-Box” decision making framework. The 4-Box model is a conceptual representation of a process designed to help predict future landscape conditions based on simulated management actions and change over time (see Figure).  In this process, the forest manager first examines the current conditions of the landscape through the lens of a particular question or management objective (e.g., where is there a need for protection or restoration?). They can then explore the predicted effects of various simulated management alternatives (e.g., thin from above, or thin from below), to see how they would affect the stand structure (e.g., stand density, basal area, and average DBH) over time, both immediately and into the future. Finally, the manager can examine how those new conditions would then affect a particular phenomenon of interest such as, severe fire risk, or wildlife habitat suitability. This process is then repeated under a different set of treatment options (scenarios) to inform the development of an effective management strategy.

 

Figure 1. The 4-Box model represents a process for evaluating future conditions based on simulated treatments and change over time.

You can check out the detailed steps to use the treatment planner using the document on the file tab. The relevant code for the treatment planner is available at github, click here to download.

Conservation Biology Institute and the Resource Conservation District of the Santa Monica Mountains are working in partnership with local land management agencies and communities to increase wildfire resilience in the Santa Monica Mountains region. The Program has the following tasks:

Raising awareness about wildfire risk in the local communities

Helping homeowners prepare for wildfire

Mapping and planning the control of flammable invasive weeds in the Woolsey Fire footprint

Modeling and research to inform planning and decision making

The Santa Monica Mountains Woolsey Fire Recovery and Adaptation Program is funded by the National Fish and Wildlife Foundation.

CBI is supporting the U.S. Forest Service (Region 8) in its efforts toward shared forest stewardship activities. Region 8 contains approximately 244 million acres of forestland, most of which (87%) is privately owned.  The Forest Service manages around 5% of the southern forests within 14 National Forests and two Special Units with other public forests make up the remaining 8%.  Because of the mixed ownership, close collaboration and shared stewardship is of paramount importance.

CBI has created a customized and curated Data Basin Gateway for the U.S. Forest Service (usfssouth.databasin.org) that supports forest stewardship organizations to access data and information to advance collaborative forest management planning. To demonstrate how to use this framework, a pilot state (North Carolina) was chosen (nc.usfssouth.databasin.org). This gateway uses the “All Lands Strategy” concept to showcase example workflows to facilitate more effective forest management and monitoring across North Carolina. CBI and the North Carolina Shared Stewardship team created supporting training materials is the form of video tutorials and how to materials.

CBI worked closely with the Natural Resource Defense Council (NRDC) to integrate relevant spatial datasets to map areas of high value from the standpoint of carbon storage and sequestration, terrestrial ecological value, and aquatic value in support of several NRDC programs, including their 30X30 campaign to protect 30% of nature in the nation by 2030. Click here to learn more about the 30×30 initiative.

Using CBI’s online modeling software called Environmental Evaluation Modeling System (or EEMS), team members were able to construct, review, and modify the models in a rigorous and highly transparent fashion from their individual remote locations. The resulting “living” models can then be used alone or together and in combination with other spatial data (e.g., existing protected areas) to add further context and insight using Data Basin. Data Basin and EEMS were effectively used to help guide NRDC’s important conservation mission.

In 2006, the Micronesia Challenge began as a commitment by the Republic of Palau, Guam, the Commonwealth of the Northern Mariana Islands, the Federated State of Micronesia, and the Republic of the Marshall Islands to preserve the marine and terrestrial resources crucial to the survival of the Pacific traditions, cultures, and livelihoods. The overall goal of the initial Micronesia Challenge was to effectively conserve at least 30% of the near-shore marine resources and 20% of the terrestrial resources across Micronesia by 2020.

During the 24th Micronesia Island Forum in 2019, the regional leadership recognized the success of the first 15 years of the Micronesia Challenge and endorsed the new Micronesia Challenge 2030 goals to effectively manage 50% of the marine resources and 30% of terrestrial resources by 2030.

In 2016, the USFS Forest Inventory and Analysis (FIA) team, regional partners and CBI developed the Micronesia Challenge Regional Terrestrial Monitoring Initiative tool (mcterrestrialmeasures.org) to allow users to visualize the spatial data from the Micronesia Challenge monitoring effort by regional framework indicator(s) that measure the status of managed conservation areas set aside under the program. The first version of the tool included forest data collected between 2003 and 2018 and determined the status and trends in forest area, forest health, understory vegetation, biomass, and carbon storage.

In this new phase of work, the Terrestrial Measure Initiative tool will be updated with the most recent data and information. The team also plans to develop a webinar presentation to communicate with local stakeholders and others about the tool and the ongoing success of the Micronesia Challenge.

The need to plan strategic, effective forest management is urgent in the southern Sierra Nevada, where forests have been ravaged by drought, fire, and catastrophic tree mortality. Multiple, sometimes conflicting, management objectives must be balanced, and multiple agencies need help coordinating their forest restoration actions. A common, readily accessible system evaluating landscape-scale forest condition is needed.

Conservation Biology Institute is working with the Sierra Nevada Conservancy, Sequoia National Forest, Sequoia National Parks, Sequoia Parks Conservancy, Save the Redwoods League, and others to develop forest resilience models and create a toolkit for exploring these data to support the planning of a range of resource management goals. These goals include the protection of sequoia groves, overall forest health, wildfire protection, and endangered species habitat management.  The project is supported by CBI’s data sharing and mapping platform Data Basin.  The project is funded by the Save the  Redwood League and Sequoia National Park through its partner the Sequoia Parks Conservancy, and CAL FIRE Forest Health Research Grant Program.

CBI recently worked with the Pacific Marine & Estuarine Fish Habitat Partnership (PMEP) to update the West Coast Estuaries Explorer, a tool designed to engage a broad range of users with detailed information about estuaries along the U.S. West Coast.  The first version of this tool was developed in partnership with PMEP and the North Pacific Landscape Conservation Cooperative.  The partnership between CBI and PMEP continues with support from NOAA and the Pacific States Marine Fisheries Commission (PSMFC).  The Estuaries Explorer got several performance and design updates to make it easier to use and more visually engaging.  In addition to the latest available information for estuary boundaries and biological habitats, the Explorer now includes aerial images for each of the estuaries in Washington, Oregon, and California.  Later this year, CBI and PMEP will incorporate additional information on the location of eelgrass habitat and areas of tidal wetland loss.  PSMFC has taken over long-term hosting of this exciting tool.

Conservation Biology Institute is a partner in a new $1 million grant from a new interdisciplinary NSF program to foster building an “open knowledge network.” The inspiration for this type of network comes from Tim Berners-Lee’s (best known founder of the World-wide Web) vision for the “semantic web,” which applies tags with relationships to information on the Internet, allowing computers to do basic reasoning for improving search results and answering questions. Apple’s Siri, Amazon’s Alexa, and Google’s Assistant all use these technologies.

Dr. John Gallo co-wrote the proposal and leads CBI’s participation in the team of 13 researchers and practitioners from 10 other institutions. The team is focused on improving access and contributions to tools for analyzing geographic data called spatial decision support systems. “The proliferation of online mapping technologies has greatly increased access to and utility of these kinds of tools, and a logical next step is increasing our ability to find the appropriate data and tools for your problem and link these together for more complex analyses,” says Principal Investigator Sean Gordon of Portland State University. Through engaging stakeholders in three applied case studies (the management of wildland fire, water quality, and biodiversity conservation), the interdisciplinary project team will develop and test participatory and automated methods for finding and sharing decision-relevant information using semantic web technologies.

The new NSF Convergence Accelerator program is named for its focus on bringing together interdisciplinary teams to address one of NSF’s 10 big ideas, specifically “Harnessing the Data Revolution“, also known as building an Open Knowledge Network. Eighteen other of these phase 1 grants were made, covering areas from molecular manufacturing to tracking potentially disruptive solar phenomena. The “accelerator” part comes from the short time frame. “The application required a 3-week turn around, which is very quick for a NSF grant,” Gordon said. “Our success was largely due to having formed the Spatial Decision Support Consortium, a professional networking group four years ago, so we had ideas and people ready to go.” Each phase 1 project is eligible to submit a phase 2 proposal for up to $5 million by next March, and the process will include giving a short “pitch” talk to a panel of experts and potential funders, much like a venture capital approach.

*Learn more about this ongoing project here.