Conservation Biology Institute is working in partnership with Riverside County Habitat Conservation Agency, Bureau of Land Management, U.S. Fish and Wildlife Service, San Diego Zoo Wildlife Alliance Academy, and others to develop and implement a rangewide conservation plan for the Stephens’ kangaroo rat (Dipodomys stephensi, SKR), a tiny rodent native to Southern California’s shrinking grassland habitats.
The SKR Rangewide Management and Monitoring Plan, developed in collaboration with species managers, researchers, and land owners, complements existing management plans rather than replaces them, recognizing that each location has unique management priorities. Coordinating across local conservation efforts will facilitate collaborative conservation action across the species’ entire range.
The SKR Plan recommends management actions to improve habitat and ameliorate threats from human activities and climate change and provides a standardized monitoring protocol to track the species’ population status and trends. CBI has developed a customized field data collection application using ArcGIS Field Maps, and our SKR data management team supports the field monitoring effort and ensures long-term integrity of the data in partnership with USFWS’ Ecosphere Program.
This work builds upon a habitat suitability model developed in 2019 by Conservation Biology Institute using Sentinel-2 satellite imagery and being updated in time to support the 2024 monitoring season. These updatable landscape-scale habitat maps are the foundation for statistically-defensible monitoring and play a key role in planning coordinated conservation of the species.
This work is funded by the U.S. Bureau of Land Management. For more information about this effort, please contact Wayne Spencer at wdspencer@consbio.org or Brian Shomo at bshomo@wrcog.us.
Please see the SKR Rangewide Management and Monitoring Website for The SKR Rangewide Management and Monitoring Plan and Protocol, as well as other documents, maps, and data from this project.
Stephens’ kangaroo rat (Dipodomys stephensi, SKR). Photo by Moose Peterson.
Conservation Biology Institute (CBI) and American Farmland Trust (AFT), the organization behind the national movement No Farms No Food®, continue to build off their previous work on the San Joaquin Land and Water Strategy. With the support from a Conservation Innovation Grant by the USDA Natural Resources Conservation Service, this phase of work focused on identifying priority areas for groundwater recharge in Madera and Stanislaus counties as well as the greater San Joaquin Valley.
CBI and AFT have compiled informative datasets related to soils, crop types, water infrastructure, and conservation areas in the San Joaquin Valley. These will be located in a San Joaquin Valley Gateway Gallery and mapping tool (with an associated manual) to aid in identifying optimal projects with the greatest potential to increase infiltration and conserve water. These datasets will provide a broad context that will allow for multiple benefits where possible such as locating conservation easements near currently protected areas in the productive farmland regions of Madera and Stanislaus counties, California.
The Project Prioritization Tool (PPT) is a conservation decision-making tool to increase the adoption of water infiltration practices, improve groundwater recharge, and protect agricultural land in the San Joaquin Valley (SJV). This PPT manual was developed by AFT and the Conservation Biology Institute (CBI) based on the data analysis of the San Joaquin Land and Water Strategy report.
Cachuma Resource Conservation District (RCD) is working in partnership with Conservation Biology Institute, LegacyWorks, and Sharyn Main Consulting on the Regional Priority Plan to Reduce Wildfire Risk and Improve Forest Health in Santa Barbara County (RPP), which is a flagship project of the Santa Barbara County Conservation Blueprint. Funded by the California Coastal Conservancy, the RPP helps with the planning, mapping, and prioritization of projects that will proactively address wildfire threat in Santa Barbara County, as well as improve forest and habitat health.
The RPP is a multi-prong collaboration, which focuses on public and private land in the wildland/urban interface (WUI). CBI is the lead for a component of the project, to develop a sophisticated decision-support mapping tool to not only predict areas of high fire-risk on a landscape scale, but also help the community prioritize where fire risk mitigation projects should occur. The tool will serve as a community resource within the Santa Barbara County Conservation Blueprint and should improve communication, network building, and support community priorities through a regional approach to fire resilience and habitat health. The project team is talking with the many agencies and stakeholders involved, and facilitating the collaborative decision-making process.
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.
The USDA Conservation Reserve Program (CRP) works with farmers and landowners to implement conservation management practices on enrolled lands, with paid contracts ranging from 10 to 15 years in length. The CRP Grasslands practices target restoration of agricultural grassland systems by augmenting native vegetation for pollinators, providing habitat for grassland plants and animals, increasing biodiversity, reducing soil erosion, and improving water quality. The USDA’s CRP has been successful in improving the conservation value of millions of acres of farmlands; however, the program currently lacks spatially explicit information on land cover and vegetation within CRP-enrolled tracts.
In partnership with the USDA FSA program, the Conservation Biology Institute (CBI) used a combination of remote sensing and machine learning algorithms deployed on the innovative cloud-computing platform, Google Earth Engine, to map grassland characteristics. We used a rich suite of enviro-climatic data, multiple sources of satellite imagery, and Random Forest modeling techniques to predict land cover for study areas in Washington, Colorado, and Kansas, where CRP Grasslands holdings are most prevalent. We used machine learning to create predictive maps of vegetation type by leveraging an extensive set of satellite-derived variables, environmental layers, and federal survey data (from BLM’s AIM and USDA NRCS’s NRI programs). Our initial investigation utilized Landsat 8 satellite data to model vegetation cover across the Washington study area and then scaled up to the Colorado-Kansas study area. The Washington study site was selected for further model enhancements and an in-depth comparison of Landsat 8, Sentinel-2, and MODIS satellite imagery, to evaluate differences in model development and performance among sensor types. We generated vegetation cover predictions for the year 2019 using Random Forest classification models. Classified outputs for the five vegetation cover models – annual grass, perennial grass, annual forb, perennial forb, and bare soil – were post-processed to exclude water and urban land cover and areas that were not relevant for mapping grasslands.
Mapped outputs showing vegetation percent cover predictions from our pilot project have been integrated into CBI’s CRP online decision support tool. This online tool offers functionality for managers and landowners to view, filter, compare and summarize geospatial information relevant for assessing CRP tracts in the study areas. You will need permission from USDA to use the tool, but it is available at https://crptool.org/. Anyone can view the design of the tool at USDA CRPtool.
You can read more details in the following publication.
Degagne, Rebecca; Pizzino, Declan; Friedrich, Hannah; Gough, Mike; Joseph, Gladwin; Strittholt, James; et al. (2022): Mapping Conservation Reserve Program Grasslands in Washington, Colorado, and Kansas with Remote Sensing and Machine Learning. figshare. Journal contribution. https://doi.org/10.6084/m9.figshare.19141853.v1
The Conservation Biology Institute and the Deschutes Trails Coalition (DTC) are in the process of developing a web-based system to assist the DTC in sustainably managing multi-use trails in Deschutes County. The collaborative process of creating a framework to support DTC’s decision making relies on modeling Environmental, Economic, Physical, and Social Sustainability of recreation activities and trails.
*Images provided by Danielle MacBain at the Deschutes Trails Coalition.
*The DTC Dashboard will include a form-based system to give users the ability to enter or modify information in the trails database.
*Mockup of the DTC Dashboard (Query Tools on the Manage Tab)
Wind energy developed in federal ocean waters off California’s coastline is poised to play an important role in diversifying the portfolio of resources that will help California achieve its 100% renewable and zero-carbon energy goals. Since 2016, the state has coordinated with other governmental partners, including the BOEM-California Renewable Energy Intergovernmental Task Force, to identify areas in federal waters off the state’s coast suitable for potential offshore wind energy development. To support this effort, the Conservation Biology Institute (CBI) is using data from the California Offshore Wind Energy Gateway to produce a robust set of spatial models, designed to synthesize information to help stakeholders and decision-makers assess the suitability of offshore wind energy development in federal waters off the coast of California. These models, created using the Environmental Evaluation Modeling System (EEMS), provide a transparent and data-driven means for assessing a range of considerations at a given location, such as existing energy potential, deployment feasibility, ocean uses, fisheries, and marine life occurrence. Together, these models can be used to inform planning processes for offshore wind energy development to maximize renewable power generation and to avoid or minimize impacts to existing ocean uses and the environment.
The California Offshore Wind Energy Modeling Platform, powered by EEMS Online technology, provides an interface where stakeholders and decision-makers can interact with and explore the models and their data sources to help support decision-making processes.
The project’s technical report, executive summary, and presentation slides are available under “Project Files”, on the right side of this page. A California Energy Commission webinar recording with a project overview can be found here.
Conservation Biology Institute specializes in harnessing the power of spatial data for conservation planning and decision-making. We create tools in close collaboration with state agencies that help them achieve their missions. Recently we’ve had the opportunity to work with the California Department of Food and Agriculture (CDFA).
The CDFA Healthy Soils Program promotes the development of healthy soils on California’s farmlands and ranch lands by providing financial incentives to California growers and ranchers to implement agricultural management practices that sequester carbon, reduce atmospheric GHGs and improve soil health.
Conservation Biology Institute created the CDFA Healthy Soils Program tool, an online tool to streamline the submission process for proposals to the Healthy Soils Program. This tool, a custom module of RePlan, allows a grant recipient to locate and map proposed conservation practices, view and select from recommended species for planting, and conform with multiple project eligibility requirements. All project components are then summarized in a proposal report for upload to the CDFA Healthy Soils Program project submission website.
*Find the tool here: https://sitecheck.opr.ca.gov/
Landscape connectivity is critical for species dispersal and population resilience. This project is part of the collaborative Landscape Conservation Design (LCD) for the Pacific Northwest coastal ecoregion and conducted in partnership with the North Pacific Landscape Conservation Cooperative. The goal is to identify connectivity pathways and prioritize corridors for 2-4 focal species West of the Cascades in Oregon and Washington. In Oregon, we will work closely with the members of the Oregon Habitat Connectivity Consortium (OHCC) for both the coastal and Willamette valley ecoregions of the state. The methods tested and refined in this project will feed into future Oregon-wide connectivity mapping.
To learn more and explore related maps and datasets, please visit the Data Basin gallery, “Connectivity of Naturalness in Western Washington“. The gallery includes outputs showing the structural connectivity (i.e. naturalness connectivity) for Western Washington.
These data can be used to help guide connectivity conservation efforts. They are the results from the pilot project comparing Omniscape (coreless) and Linkage Mapper (core areas) modeling methods. Extra attention was made to the data inputs and the rigor of the analyses so that the results can be applied, in addition to answering the driving research question.