Landscape Analysis
PhenoMap is a new Web-based tool that managers can use to assess the production and location of high-quality forage. It uses satellite imagery to address the need for near-real-time information about plant life cycle events over large spatial areas. “This remote sensing tool can help prioritize management of rapidly degrading resources across the landscape, in near real time,” says Nancy Grulke, a PNW research ecologist with the project. “Tracking resource quality from week to week with imagery can not only support management decisions with empirical evidence, but also provide a visual tool for communication with landowners.”
Access Western Migrations Tool
In 2018, the U.S. Geological Survey assembled a Corridor Mapping Team to assist western states in mapping bison, elk, moose, mule deer, and pronghorn corridors using existing GPS data. One outcome of the team is this mapping tool, which provides public access to data on migrating ungulates through a unique partnership between participating western states. This tool enables viewing of mapped migration corridors, routes, stopovers and ranges. Choices for base maps include land cover and land management. In addition, users can add their own zipped ArcGIS shapefile to the viewer through the “Add Data” button. Email user questions to: westernmigrations@uwyo.edu.
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This study showed that all models provided higher predictive accuracy than chance, with an average AUC across the 20 forage species of 0.84 for distal and proximal variables and 0.81 for proximal variables only. This indicated that the addition of distal variables improved model performance. We validated the models using two independent datasets from two regions of Idaho. We found that predicted forage species occurrence was on average within 10% of observed occurrence at both sites. However, predicted occurrences had much less variability between habitat patches than the validation data, implying that the models did not fully capture fine-scale heterogeneity. We suggest that future efforts will benefit from additional fine resolution (i.e., less than 30 m) environmental predictor variables and greater accounting of environmental disturbances (i.e., wildfire, grazing) in the training data. Our approach was novel both in methodology and spatial scale (i.e., resolution and extent). Our models can inform ungulate nutrition by predicting the occurrence of forage species and aide habitat management strategies to improve nutritional quality.
This study modelled 20 forage species that are suitable for mule deer and Rocky Mountain elk. Climatic, topographic, soil, vegetation, and disturbance variables were attributed to approximately 44.3 million habitat patches generated using multi-scale object-oriented image analysis. Lasso logistic regression was implemented to produce predictive SDMs. The study evaluated if the inclusion of distal environmental variables (i.e., indirect effects) improved model performance beyond the inclusion of proximal variables (i.e., direct physiological effect) only. Results showed that all models provided higher predictive accuracy than chance, with an average AUC across the 20 forage species of 0.84 for distal and proximal variables and 0.81 for proximal variables only. This indicated that the addition of distal variables improved model performance. The study validated the models using two independent datasets from two regions of Idaho and found that predicted forage species occurrence was on average within 10% of observed occurrence at both sites.
Webinar recording.
In this webinar, RMRS research ecologist Sean Healey will discuss improved techniques for mapping forest disturbance and recovery across the United States with remotely sensed data.
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In recent decades, many bumble bee species have declined due to changes in habitat, climate, and pressures from pathogens, pesticides, and introduced species. The western bumble bee (Bombus occidentalis), once common throughout western North America, is a species of concern and will be considered for listing by the U.S. Fish and Wildlife Service (USFWS) under the Endangered Species Act (ESA). We attempt to improve alignment of data collection and research with USFWS needs to consider redundancy, resiliency, and representation in the upcoming species status assessment. We reviewed existing data and literature on western bumble bee, highlighting information gaps and priority topics for research. Priorities include increased knowledge of trends, basic information on several life‐history stages, and improved understanding of the relative and interacting effects of stressors on population trends, especially the effects of pathogens, pesticides, climate change, and habitat loss. An understanding of how and where geographic range extent has changed for the two subspecies of western bumble bee is also needed.
Using data collected from 120 plots over three years (2011–2013) and 2012 National Agriculture Imagery Program (NAIP) imagery, we evaluated (1) whether well pads are more likely to be located in areas of pygmy rabbit habitat, (2) whether the presence and abundance of pygmy rabbits are related to distance from infrastructure, and, if so, (3) how much of the total surface area on a gas field is affected. Well pads on three gas fields occurred in higher quality pygmy rabbit habitat than did a set of randomly generated points, and the abundance and probability of pygmy rabbits being present were lower within approximately 0.5–1.5 km of the nearest road and 2 km of well pads and utilities. Buffering a digital layer of roads and well pads on one gas field revealed that nearly 82% of the (4417 km2) surface area was within 1 km of infrastructure, and over 95% of the gas field surface area was within 2 km. This need not be the case on future gas fields. Directional and horizontal well drilling technologies now make it possible for gas to be recovered from a greater area per well pad, enabling future gas field developments that require fewer well pads, roads, and pipeline corridors. Such changes would enable increased well pad spacing and provide the opportunity to locate gas field infrastructure in areas of poor quality wildlife habitat, avoid high priority habitat, and conserve a greater amount of on‐field wildlife habitat overall.
This work represents an effort by the NRCS and USFS to rapidly quantify the impact of drought on vegetation production across large areas to inform a reseeding strategy for affected areas. As a result of this collaboration 1.5 million hectares (3.7 million acres) in three counties were identified as exhibiting 50% losses in production or greater. During future drought declarations, this technology may be deployed to rapidly determine the impacts of the drought and identify the hardest hit areas. Additionally, RPMS can be applied to identify areas developing drought conditions and recovering from drought. Information produced by this process can be an important component to management strategies, adding to manager expertise and drought plans. When used in conjunction with other sources of information, such as drought monitors, this process provides a rapid, cost-effective, transparent solution to a long-standing problem and demonstrates a unique way that multiple agencies can team together to help producers and land managers in the western United States. This type of analysis is inherently multijurisdictional and embraces the “Shared Stewardship”28 vision and leverages multiagency resources from the NRCS and USFS to combat the effects of drought.
Description: The first webinar in a series of virtual learning opportunities that address the cultural shifts and adaptations that are being embraced at all levels to evolve and advance progress toward the vision and goals of the Cohesive Wildland Fire Strategy.
Presenter: Alan Ager, Research Forester, USFS Rocky Mountain Research Station