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Christopher T. Domschke

Publications and source records attributed to Christopher T. Domschke.

3 recordsLinked to original sources

Climate consideration in environmental effects analyses on federal public lands in the United States

Effects of a changing climate, including drought, wildfire, and invasive species encroachment, are evident on public lands across the United States. Decision making on Federal public lands requires analyses under the National Environmental Policy Act (NEPA), and there are guidelines for considering climate in NEPA analyses. To better understand how climate most recently has been considered, we analyzed a stratified random sample of 130 environmental assessments (EAs) completed by the Bureau of Land Management (BLM) from 2021 to 2023 across the contiguous United States. We assessed whether EAs considered (1) potential effects of the proposed action on climate (2) potential climate effects on the proposed action, and (3) potential climate effects on resources of concern. We also identified whether EAs included data and science about climate or greenhouse gas emissions, and which datasets and documents were cited. We used two approaches: automated keyword searches and document analysis. Thirty-seven percent of EAs considered the potential effects of the proposed action on climate, 8% considered the potential effects of climate on the proposed action, and 4% of individual resource analyses considered the potential effects of climate on the resource. EAs in the ‘oil and gas development,’ ‘renewable energy,’ and ‘forestry and timber management’ proposed action categories most frequently considered the potential effects of climate and used climate data and science. Our findings suggest an opportunity for scientists to work more closely with public land managers to identify available data and science for considering climate in environmental effects analyses and to provide science delivery mechanisms that can facilitate the consideration and use of climate science in decision making.

Environmental Management

Effects of nonmotorized recreation on ungulates in the western United States—A science synthesis to inform National Environmental Policy Act analyses

The U.S. Geological Survey is working with Federal land management agencies to develop a series of science syntheses to support National Environmental Policy Act (NEPA) analyses. This report synthesizes science information about the potential effects of nonmotorized recreation on ungulates in the western United States. We conducted a structured literature search to find published science, data, and analysis methods about the characteristics of nonmotorized recreation, ungulate exposure and response to nonmotorized recreation, and approaches to mitigate negative effects of nonmotorized recreation on ungulates. The sections of the report align with standard elements of the NEPA analysis process. We found that timing, intensity, duration, and spatial distribution of nonmotorized recreation are important factors to understand when assessing effects of recreation on ungulates. Several aspects of ungulate biology, which vary by species, population, and individual, affect ungulate susceptibility to effects from recreation, including diet, migration and movement, and seasonal biology. Techniques for assessing effects include basic spatial analyses based on buffers around trails and recreation sites and more technical analytical methods based on displacement or avoidance of recreation sites. Options for mitigating negative effects of nonmotorized recreation on ungulates include timing and type-of-use restrictions, recreator education, and project design features to avoid human-ungulate conflicts. Public land managers can use this report by incorporating it by reference in NEPA analyses or as a general reference to find literature or identify gaps in the literature about the effects of noise from nonmotorized recreation on ungulates.

Scientific Investigations Report

Mapping predicted ecological states at landscape scales using remote sensing data and machine learning

Dryland ecosystems, covering 45% of the Earth's land and supporting over one-third of the global population, face significant threats from land degradation and ecological state change. Managing these ecosystems is complex, and science-based frameworks like Ecological Site Descriptions and state-and-transition models are essential tools for guiding decisions to support ecological health while maintaining stakeholder values such as grazing, wildlife, and recreation. However, alignment of these frameworks with smaller scale soil survey maps limits their applicability to broader ecological processes. Here, we extend these frameworks to larger landscapes with a machine learning approach that integrates large-scale, high-resolution vegetation data with identified ecological states from a data-driven state-and-transition model developed for a landscape-scale Ecological Site Group. A “global” model, which used combined inputs from multiple remotely sensed datasets, outperformed individual dataset models based on evaluation with independent data. Ecological state maps generated through this approach broaden the utility of state-and-transition models across Ecological Site Groups, providing a more spatially robust tool for land management at watershed and larger landscape scales. These methods, and the associated ecological state maps, can help meet critical needs for improved land condition assessments that support development of resource management plans and help identify priority areas for restoration and conservation.

Arizona, Colorado, New Mexico, Utah, Wyoming