Geology topics

R. Quinn Thomas

Publications and source records attributed to R. Quinn Thomas.

5 recordsLinked to original sources

USGS2024

Near-term ecological forecasting for climate change action

A substantial increase in predictive capacity is needed to anticipate and mitigate the widespread change in ecosystems and their services in the face of climate and biodiversity crises. In this era of accelerating change, we cannot rely on historical patterns or focus primarily on long-term projections that extend decades into the future. In this Perspective, we discuss the potential of near-term (daily to decadal) iterative ecological forecasting to improve decision-making on actionable time frames. We summarize the current status of ecological forecasting and focus on how to scale up, build on lessons from weather forecasting, and take advantage of recent technological advances. We also highlight the need to focus on equity, workforce development, and broad cross-disciplinary and non-academic partnerships.

Nature Climate Change↗

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↗

USGS2023

The NEON Ecological Forecasting Challenge

The 21st century continues to be characterized by major changes to the environment and the ecosystem services upon which society depends. Anticipating and responding to these changes requires that scientists explicitly forecast future conditions in real time (Dietze et al . 2018 ). Ecological forecasting, like weather and epidemiological forecasting, involves integrating data and models to generate quantitative predictions of the future state of ecological systems before observations are collected. The iterative cycle of creating forecasts, evaluating them with new observations, updating the models, and then making new forecasts has the potential to accelerate learning across many ecological subdisciplines. This cycle builds on openly available data, often published soon after collection, as is increasingly common in ecological observatory networks, such as the National Ecological Observatory Network (NEON). To accelerate improvements in ecological forecasting, we designed and launched the NEON Ecological Forecasting Challenge (hereafter, “Challenge”) (Figure 1 ), an open platform for the ecological and data science communities to forecast NEON data before they are collected.

Frontiers in Ecology and the Environment↗

USGS2022

Physics-guided architecture (PGA) of LSTM models for uncertainty quantification in lake temperature modeling

This chapter focuses on meeting the need to produce neural network outputs that are physically consistent and also express uncertainties, a rare combination to date. It explains the effectiveness of physics-guided architecture - long-short-term-memory (PGA-LSTM) in achieving better generalizability and physical consistency over data collected from Lake Mendota in Wisconsin and Falling Creek Reservoir in Virginia, even with limited training data. Even though PGL formulations result in improvements in the generalization performance and lead to machine learning (ML) predictions that are more physically consistent, simply adding the physics-based loss function in the learning objective does not overcome the black-box nature of neural network architectures, which often involve arbitrary design choices. The temperature of water in a lake is a fundamental driver of lake biogeochemical processes, and it controls the growth, survival, and reproduction of fishes in the lake.

Virginia, Wisconsin↗

USGS2018

Growth and survival relationships of 71 tree species with nitrogen and sulfur deposition across the conterminous U.S.

Atmospheric deposition of nitrogen (N) influences forest demographics and carbon (C) uptake through multiple mechanisms that vary among tree species. Prior studies have estimated the effects of atmospheric N deposition on temperate forests by leveraging forest inventory measurements across regional gradients in deposition. However, in the United States (U.S.), these previous studies were limited in the number of species and the spatial scale of analysis, and did not include sulfur (S) deposition as a potential covariate. Here, we present a comprehensive analysis of how tree growth and survival for 71 species vary with N and S deposition across the conterminous U.S. Our analysis of 1,423,455 trees from forest plots inventoried between 2000 and 2016 reveals that the growth and/or survival of the vast majority of species in the analysis (n = 66, or 93%) were significantly affected by atmospheric deposition. Species co-occurred across the conterminous U.S. that had decreasing and increasing relationships between growth (or survival) and N deposition, with just over half of species responding negatively in either growth or survival to increased N deposition somewhere in their range (42 out of 71). Averaged across species and conterminous U.S., however, we found that an increase in deposition above current rates of N deposition would coincide with a small net increase in tree growth (1.7% per Δ kg N ha -1 yr -1 ), and a small net decrease in tree survival (-0.22% per Δ kg N ha -1 yr -1 ), with substantial regional and among-species variation. Adding S as a predictor improved the overall model performance for 70% of the species in the analysis. Our findings have potential to help inform ecosystem management and air pollution policy across the conterminous U.S., and suggest that N and S deposition have likely altered forest demographics in the U.S.

PLoS ONE↗