Geology topics

Jaime Ashander

Publications and source records attributed to Jaime Ashander.

3 recordsLinked to original sources

USGS2025

RAD (Resist-Accept-Direct) switch points and triggers for adaptation planning

Climate change is transforming ecosystems globally. The Resist-Accept-Direct (RAD) framework has gained traction within many natural resource management institutions to help consider the decision space in response to this transformation. Because RAD helps manage for directional change, RAD choices entail considering which RAD pathway to implement and for how long. For example, one may accept a slowly changing ecosystem, but at a certain point, decide to begin resisting or directing the change an ecosystem is experiencing. Alternatively, one may begin resisting an ecosystem transformation, but ultimately realize resistance is no longer feasible based on cost or efficacy. These choices are challenging and encompass broad domains of cultural, ecological, financial, organizational, public, regulatory, and technological considerations to determine when to switch RAD pathways. We introduce the concepts of RAD switch points and triggers to help support these decision processes. We illustrate these concepts using case studies on walleye ( Sander vitreus ) stocking decisions in Wisconsin, wildfire response in the Greater Yellowstone Ecosystem, and bull trout ( Salvelinus confluentus ) management in Oregon, USA. Synthesizing across these examples, we delineate key points for decision makers as they (iteratively) reevaluate among the RAD pathways as conditions continue to change.

Journal of Environmental Management↗

USGS2023

A community convention for ecological forecasting: Output files and metadata version 1.0

This paper summarizes the open community conventions developed by the Ecological Forecasting Initiative (EFI) for the common formatting and archiving of ecological forecasts and the metadata associated with these forecasts. Such open standards are intended to promote interoperability and facilitate forecast communication, distribution, validation, and synthesis. For output files, we first describe the convention conceptually in terms of global attributes, forecast dimensions, forecasted variables, and ancillary indicator variables. We then illustrate the application of this convention to the two file formats that are currently preferred by the EFI, netCDF (network common data form), and comma-separated values (CSV), but note that the convention is extensible to future formats. For metadata, EFI's convention identifies a subset of conventional metadata variables that are required (e.g., temporal resolution and output variables) but focuses on developing a framework for storing information about forecast uncertainty propagation, data assimilation, and model complexity, which aims to facilitate cross-forecast synthesis. The initial application of this convention expands upon the Ecological Metadata Language (EML), a commonly used metadata standard in ecology. To facilitate community adoption, we also provide a Github repository containing a metadata validator tool and several vignettes in R and Python on how to both write and read in the EFI standard. Lastly, we provide guidance on forecast archiving, making an important distinction between short-term dissemination and long-term forecast archiving, while also touching on the archiving of code and workflows. Overall, the EFI convention is a living document that can continue to evolve over time through an open community process.

Ecosphere↗

USGS2022

The power of forecasts to advance ecological theory

Ecological forecasting provides a powerful set of methods for predicting short- and long-term change in living systems. Forecasts are now widely produced, enabling proactive management for many applied ecological problems. However, despite numerous calls for an increased emphasis on prediction in ecology, the potential for forecasting to accelerate ecological theory development remains underrealized. Here, we provide a conceptual framework describing how ecological forecasts can energize and advance ecological theory. We emphasize the many opportunities for future progress in this area through increased forecast development, comparison and synthesis. Our framework describes how a forecasting approach can shed new light on existing ecological theories while also allowing researchers to address novel questions. Through rigorous and repeated testing of hypotheses, forecasting can help to refine theories and understand their generality across systems. Meanwhile, synthesizing across forecasts allows for the development of novel theory about the relative predictability of ecological variables across forecast horizons and scales. We envision a future where forecasting is integrated as part of the toolset used in fundamental ecology. By outlining the relevance of forecasting methods to ecological theory, we aim to decrease barriers to entry and broaden the community of researchers using forecasting for fundamental ecological insight.

Methods in Ecology and Evolution↗