Enhancing Customer Experience, improving assessments, and streamlining workflows 

The pilot version of the toolkit was designed to enhance customer experience, and better manage CRP enrollment and assessments. It initially covers the states of Colorado, Kansas, Nebraska, North Dakota, South Dakota, and Washington. The USDA CRP toolkit (Pilot) was developed with a grant from USDA FSA to Conservation Biology Institute (CBI). It was designed incorporating extensive inputs from USDA staff at the federal, state and county offices, producers, and vendors. More states will be added over time as stakeholders find value and funding is available. 

The version 1.0 of the current set of three tools that are released are publicly available here.

The map-based Plant Finder tool allows you to easily find plants suitable for your location that meet particular growing conditions (e.g., ecological sites, soil conditions, moisture requirements, and specific environmental tolerances) as well as provide specific benefits (e.g., livestock browse, wildlife habitat, and pollinator support). Once a species list is generated, the Vendor Match tool identifies where seeds and/or seedlings from your species list can be purchased. The Vendor Browse tool allows you to easily browse a map view of the registered vendors in your state or across the country where seeds and seedlings can be purchased. 

All data content is from authoritative sources, including NRCS state resources, NRCS Ecological Site program, and the USDA Plant database. 

In the future, in partnership with the Institute for Applied Ecology, CBI is planning to add extra functionality and expand the reach of the vendor match tool nation wide,  including members of the Native Seed Network.

The USDA Conservation Reserve Program (CRP) works with private landowners to advance conservation on their lands. This voluntary program currently comprises over 23 million acres making it an extremely important component of conservation in America, particularly in locations with limited public conservation lands. Management actions taken on enrolled lands include augmenting native vegetation for pollinators, providing habitat for grassland plants and animals, increasing biodiversity, reducing soil erosion, and improving water quality.

CBI is working with USDA to improve the effectiveness and efficiency of the program as it strives to expand enrollment in the coming years. Working in several pilot states (Mississippi, Kansas, Colorado, Washington, Nebraska, and the Dakotas), CBI is focusing on the following tasks:

• Create an online service open to the public that assists landowners to identify plant mixes most appropriate for their locations and management goals and vendors where seeds/seedlings can be acquired.
• For agency and CRP contract holders, create an online service that helps manage emergency haying/grazing requests.
• Develop and implement technology and protocols (e.g., phone field assessment app) that will involve contract holders more in the implementation of their agreements, which will provide important monitoring for improved program evaluation.
• Improve mapping and monitoring of forests and grasslands as they relate to CRP goals and objectives using the latest remote sensing technology, machine learning, and high-end processing resources.
• Conduct spatial analyses to help the program prioritize areas for more targeted enrollment.
• For agency users, provide an online service that allows for rapid and meaningful assessments of conservation management outcomes on CRP lands.

CBI Takes a Field Trip!

CBI headed to Jamestown, North Dakota, to speak with producers and USDA county staff regarding the USDA Mobile App, a tool designed for self-reporting, assessment, and communication surrounding CRP fields. We tested the app on over 35 fields to incorporate feedback from on-the-ground users, train remote sensing data, and test the effectiveness and ease-of-use of the tool. We coordinated with USGS, as well, testing this app on several of their pollinator plots, in conjunction with another CBI project.

Additionally, CBI traveled to Bismark and spoke with state NRCS staff, in order to hone and edit management questions and expand the usefulness of the app for reporting requirements and data gathering. Our experience was invaluable, as truthful reactions and deliberate responses from those who will eventually be using the tool, are critical in creating something that will be utilized and, ultimately, successful.

The Conservation Biology Institute’s Environmental Evaluation Modeling System (EEMS) is a fuzzy-logic modeling framework that can be applied to a wide range of conservation and planning applications. With EEMS, data of different types and formats from multiple sources can be combined to answer questions such as current and future habitat value, ecological/development conflict, and landscape vulnerability to climate change. 

To use EEMS, a user builds a tree-based logic model in which the bottom nodes represent the initial data inputs. These data values are converted into fuzzy values (based on the premise that each input value can be represented by a value ranging from -1 for fully false to +1 for fully true). Fuzzy logic operations (analogous to basic logic operations such as AND and OR) are then used to combine nodes hierarchically until a final value representing the answer to a research question, (e.g., what is the relative value of endangered species habitat across our study area?), is produced.

There are many advantages to this data-driven modeling approach: (1) it’s highly interactive and flexible; (2) it’s easy to visualize data sources and analysis structure; (3) components are modular, making it easy to add or exclude information; (4) model parameters can be adjusted using a number of different mechanisms; and (5) numerous data of different types can be included into a single integrated analysis.

EEMS modeling has been applied by CBI scientists in a wide range of planning and ecological applications. In the Tehachapis and Southern Sierra, a model incorporating data for habitat presence, habitat linkage, and disturbance was used to find areas of high ecological value and to provide guidance for reserve design to inform siting renewable energy infrastructure. For the Bureau of Land Management Rapid Ecological Assessments of the Sonoran Desert and Colorado Plateau ecoregions, EEMS models were developed and used to evaluate a variety of current and projected ecological metrics such as landscape and aquatic intactness. This approach has also been used to support the State of California’s offshore wind renewable energy planning process via the development of EEMS models to help assess a range of important considerations such as wind energy potential, deployment feasibility, ocean uses, fisheries, and marine life occurrence.

EEMS Visualization and Exploration Tools

EEMS Explorer is a tool integrated into CBI’s Data Basin platform that allows users to examine EEMS models in a dynamic web-mapping environment. It provides an interactive model diagram coupled with a map display that allows users to “drill down” on an EEMS model to examine its structure, data, and logical relationships. This gives scientists and land managers the ability to determine what landscape metrics are driving model results at any given location.

EEMS Online provides another avenue for users to visualize and interact with EEMS models in a web environment. In addition to providing a rich and powerful set of tools for examining a model’s structure, data, and logical relationships, it helps facilitate the EEMS model development process by allowing users to modify a model’s parameters (e.g., weights, thresholds, and operators) to see how these changes affect the results. The modified model can then be shared among project team members, collaborators, and stakeholders for evaluation and feedback.

The combination of EEMS, EEMS Explorer, and EEMS Online provides a powerful system for the development, dissemination, and evaluation of complex models in a way that is both transparent and accessible. By helping to bridge the gap between science and technology, CBI’s EEMS modeling platform has become an important tool in helping to guide and inform conservation management and planning decisions. 

Getting Started

EEMS is written in Python and maintained as a collection of libraries within MPilot– a plugin-based, environmental modeling framework developed by CBI.

EEMS Pro is an ArcGIS implementation of EEMS that interfaces with these libraries and allows users to construct models visually using ESRI’s ModelBuilder environment. 

Models can be developed using either platform, however EEMS Pro is recommended for those with experience with ArcGIS and Model Builder.

EEMS Pro is compatible with both ArcGIS Desktop (>=10.6) and ArcGIS Pro. To get started with EEMS Pro, download the latest version here, which includes the EEMS Pro toolbox, a manual with installation instructions, and a set of tutorial data.

Learn More

See “Related Resources” on the upper right of this page. 

For an introduction to EEMS in ArcGIS, an explanation of the fuzzy logic concepts EEMS uses, and a demonstration of the EEMS Model Explorer, please click here to view a recorded webinar.

To view a recorded webinar titled “Decision-support for regional reserve design and siting of renewable energy and infrastructure”, please click here.

The Wildfire Resilience Planner: Strategic Planning at the Landscape Scale

Sonoma Water, Conservation Biology Institute (CBI), Ag Innovations, Pepperwood Preserve, Tukman Geospatial, Digital Mapping Solutions have created the Sonoma County Wildfire Resilience Planner, an online decision support tool for prioritizing locations to reduce wildfire fuels to protect lives and property, community infrastructure, ecosystem services, and biodiversity. The tool encourages collaborative planning of projects on public and private land, helping to leverage individual efforts for a unified and strategic approach to fuels management. The Wildfire Resilience Planner is available for use at https://sonoma.resilienceplanner.org/. For more information or to provide feedback, please contact Molly Oshun, Molly.Oshun@scwa.ca.gov or Deanne DiPietro, deanne.dipietro@consbio.org.

Above: the Sonoma County Wildfire Resilience Planner provides the user with the spatial data and analysis tools to identify high-priority locations for wildland fuels management.

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.  CBI developed this tool in partnership with the Pacific Marine & Estuarine Fish Habitat Partnership (PMEP). The partnership is supported by NOAA and the Pacific States Marine Fisheries Commission (PSMFC).  The Explorer includes aerial images for each of the estuaries in Washington, Oregon, and California. 

These highly dynamic systems, where rivers meet the ocean, provide many important ecosystem services. They provide essential habitat for a wide range of species, including fish, shellfish, shrimp, and crabs. Estuaries have also been significantly altered and degraded by human activities. Many along the West Coast are in poor condition or have lost significant habitat.

This viewer allows you explore estuaries along the coastline of Washington, Oregon, and California. Learn more about estuaries and their role in providing habitat for key species. Get involved to help restore these important ecosystems.

Species Potential Habitat Tool (SPHT) allows managers to identify suitable species for specific sites under current climates and a range of future climate change scenarios. The tool allows forest managers to select species to plant or to promote using other silvicultural activities such as natural regeneration or thinning. Thus, the SPHT can help promote the transition of forest to species compositions that are better adapted to future climates. The SPHT may be used in conjunction with the Seedlot Selection Tool (SST) to allow users to explore options for both species-level and within-species assisted migration. Currently the tool has data for five key species (e.g., Douglas-fir, lodgepole pine, ponderosa pine, Sitka spruce, and Engelmann spruce) in western North America, and this will eventually be expanded to 42 tree species.

Environmental Risk Screening Tool will help guide Chinese international investment projects. The overarching goal of the tool is to significantly reduce negative environmental impacts resulting from Chinese development projects around the world. The screening tool includes interactive mapping of biodiversity and environmental data against which potential development projects can be evaluated. The tool includes a standard Biodiversity Impact Analysis using a set of internationally recognized datasets (e.g., Key Biodiversity Areas, Critical Natural Habitat, Alliance for Zero Extinction sites, and Protected Areas). The tool will also include regional and country-level biodiversity and environmental data in priority countries. It is scalable for any region in the world. The tool is not available for viewing at present. But here is a powerpoint describing its capabilities.

A collaborative project involving local stakeholders that identified Priority Conservation Areas

The Conservation Biology Institute partnered with The US Fish and Wildlife Service, Refuges Lands Division and North Pacific LCC, to develop an interactive web-based mapping tool to support the Willamette Valley Conservation Study (WVCS). This tool is targeted to conservation partners in the region, as well as the general public. The primary objective of this tool is to serve as a communication and data exploration tool for priority areas identified within the WVCS, and will allow users to understand the key characteristics of each priority area and better understand why each area was selected.

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. Forest data were collected between 2003 and 2018 and are now being used to determine the status and trends in forest area, forest health, understory vegetation, biomass, and carbon storage.

The Micronesia Challenge is 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 that are crucial to the survival of the Pacific traditions, cultures and livelihoods. The overall goal of the Micronesia Challenge is to effectively conserve at least 30% of the near-shore marine resources and 20% of the terrestrial resources across Micronesia by 2020.

Linkage Mapper is a GIS toolbox designed to support regional wildlife habitat connectivity analyses. It consists of several Python scripts, packaged as an ArcGIS toolbox, that automate mapping of wildlife habitat corridors. The toolbox is comprised of six tools, described below. 

The primary and original tool in the toolbox is Linkage Pathways. Linkage Pathways uses GIS maps of core habitat areas and resistances to identify and map linkages between core areas. Each cell in a resistance map is attributed with a value reflecting the energetic “cost”, (i.e. difficulty and mortality risk) of moving across that cell. Resistance values are typically determined by cell characteristics, such as land cover or housing density, combined with species-specific landscape resistance models. As animals move away from specific core areas, cost-weighted distance analyses produce maps of total movement resistance accumulated.

The Linkage Pathways tool identifies adjacent (neighboring) core areas and creates maps of least-cost corridors between them. It then mosaics the individual corridors to create a single composite corridor map. The result shows the relative value of each grid cell in providing connectivity between core areas, allowing users to identify which routes encounter more or fewer features that facilitate or impede movement between core areas. Linkage Pathways also produces vector layers that can be queried for corridor statistics.