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Judy L. Perkins

Publications and source records attributed to Judy L. Perkins.

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Best practices for incorporating climate change science into Department of the Interior analyses, consultations, and decision making

The purpose of this document is to provide technical guidance, practical application examples, and resource lists for those who conduct, manage, and/or interpret technical workflows within the Department of the Interior. This document is intended to support implementation of Department of the Interior policy 526 DM 1 and establish best practices for using climate change science to inform analysis, consultation, and decision making. The Earth’s climate is an interconnected system that distributes energy, heat, and water around the planet. Due to human-driven increases in long-lived greenhouse gases, the Earth’s climate is now changing. For Departmental decision-making purposes, assuming a static, unchanging baseline climate is no longer consistent with current knowledge about the climate system. There are uncertainties about future climate and how resources or assets (RoAs) will respond to new conditions. To depict the possibilities, the global climate science community develops scenarios and models to explore how future climate may respond to socioeconomic and technological development in the world. Principles for informing policy development, planning and decisions, and regulatory processes using climate change science must: 1) consider the effects of future climate change, 2) characterize the risks, and 3) characterize the uncertainties. Best practices include: Use multiple scenarios to assess risks from a range of plausible societal pathways. When constraints prevent the use of multiple scenarios or if decision makers are risk averse, ensure that the chosen scenario considers higher risk outcomes. This is particularly important for large investments or irreversible decisions and reduces the chances of overconfident decision making. Use multiple climate models within each scenario to account for the range of outcomes due to model uncertainty. Do not rely solely on a single model or an ensemble average. Use relevant climate data . Use a time-period for model projections of the future climate change consistent with the relevant timeframe of the policy, action, or decision being considered. Historical observations are useful for understanding past conditions and climate trends for the next several years, but not beyond the next decade. Consult with climate data and modeling experts to assess which data and model resources are most appropriate for any given application. Clearly describe key analysis uncertainties (including with any climate observations, models, and scenarios used), and how they were addressed in the analysis and/or decision process. This ensures transparency and learning among analysts and decision makers.

Report

Biotic and abiotic treatments as a bet-hedging approach to restoring plant communities and soil functions

Two related concepts in restoration ecology include the relative interchangeability of biotic and abiotic restoration treatments for initiating recovery and bet hedging using multiple restoration approaches to increase the likelihood of favorable restoration outcomes. We used these concepts as a framework to implement a factorial experiment including biotic (outplanting greenhouse-grown individuals of three perennial species) and abiotic treatments (constructing microtopography or vertical mulch consisting of upright, dead plant material). These treatments were designed to stimulate native plant recruitment and reverse soil degradation at four disturbed sites in the Sonoran Desert, U.S.A. The first growing season after the restoration treatments was the driest of the last 47 years, and 100% of outplants died. While the biotic treatment failed, the vertical mulch abiotic treatment increased native shrub seedling cover at the driest site and reversed soil loss across sites by increasing soil accumulation by 6× to 2 cm/year. Results revealed that (1) inexpensive, minimal-input abiotic treatments outperformed resource-intensive biotic treatments; (2) the restoration effort withstood the total failure of a major component (outplanting) to nevertheless achieve key restoration benefits within 2–3 growing seasons; and (3) incorporating multiple treatment types served as a bet-hedging approach to buffer against treatment failures. Integrating minimal-input abiotic treatments in restoration warrants consideration given their low cost and bet-hedging potential.

Restoration Ecology