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Jeremy White

Publications and source records attributed to Jeremy White.

8 recordsLinked to original sources
USGS2026

White light reduces flight height of birds in low-density migration corridor

Artificial light at night (ALAN) has been shown to influence the behavior of migratory birds, yet how different light spectra modulate these effects remains unclear. We conducted a field experiment for 31 nights at a remote site in northern Colorado, USA, using 2 800-W light-emitting diode (LED) floodlights with white, red, amber, and blue lighting treatments during fall migration in 2023 and 2024. Using a vertical-looking radar system, we quantified avian in-flight responses in migration traffic rate, flight height, and flight direction between paired control periods with no light and periods with lighting treatments. Although we found no effects of our treatments on migration traffic rate and weak effects on flight direction, we found that broad-spectrum white light with emissions across a 400–600 nm spectrum reduced mean avian flight height by an estimated 22.57 m (±10.53 SE) compared to dark periods, and this response was stronger than red and blue lighting treatments. Our ability to detect behavior changes from a point source in a rural, low-density migration system supports the notion that the effects of artificial light at night on bird behavior may be more pervasive than is often recognized, and that this varies by ALAN spectra. Beyond providing insight into avian orientation and in-flight behavior, our results have implications for future research, the conservation management of artificial light at night, and lighting designs that minimize effects on migratory birds.

Colorado↗
USGS2024

Parameter ESTimation with the Gauss–Levenberg–Marquardt algorithm: An intuitive guide

In this paper, we review the derivation of the Gauss–Levenberg–Marquardt (GLM) algorithm and its extension to ensemble parameter estimation. We explore the use of graphical methods to provide insights into how the algorithm works in practice and discuss the implications of both algorithm tuning parameters and objective function construction in performance. Some insights include understanding the control of both parameter trajectory and step size for GLM as a function of tuning parameters. Furthermore, for the iterative Ensemble Smoother (iES), we discuss the importance of noise on observations and show how iES can cope with non-unique outcomes based on objective function construction. These insights are valuable for modelers using PEST, PEST++, or similar parameter estimation tools.

Groundwater↗
USGS2023

Effects of auto-adaptive localization on a model calibration using ensemble methods

Simulations of the natural systems for environmental decision-making typically benefit from a highly parameterized approach (Hunt et al. 2007; Doherty and Hunt 2010), which enhances the flow of information contained in state observations to the parameters and improves application to decision support. However, parameter estimation (PE) with highly parameterized environmental models using traditional approaches (e.g., Doherty and Hunt 2010) is computationally intensive. Attempts at addressing the computational burden include improved computing approaches (e.g., Schreüder 2009; Hunt et al. 2010) and advances in algorithmic approaches (e.g., Tonkin and Doherty 2005; Welter et al. 2012, 2015). Recently, the iterative ensemble smoother (IES) approach (Chen and Oliver 2013; White 2018; White et al. 2020a) has greatly improved the efficiency of the PE calibration process compared to previous algorithms while concurrently providing nonlinear estimates of uncertainty (Hunt et al. 2021).

Groundwater↗
USGS2022

A model-independent tool for evolutionary constrained multi-objective optimization under uncertainty

An open-source tool has been developed to facilitate constrained single- and multi-objective optimization under uncertainty (CMOU) analyses. The tool uses the well-known PEST interface protocols to communicate with the underlying forward simulation, making it non-intrusive. The tool contains a built-in parallel run manager to make use of heterogeneous and distributed computing resources. Several popular and well-known evolutionary algorithms are implemented and can be combined with a range of approaches to represent uncertainty in model-derived constraint/objective values. These attributes serve to address the current barrier to adopt advanced CMOU analyses for a wide range of decision-support problems across the environmental modeling spectrum. We demonstrate the capabilities of the CMOU tool on a well-known analytical benchmark problem that we augmented to include uncertainty, as well as on a synthetic density-dependent coastal groundwater management benchmark problem. Both demonstrations highlight the importance of explicitly accounting for uncertainty to convey risk and reliability in pareto-optimal design.

Environmental Modelling & Software↗
USGS2021

Towards improved environmental modeling outcomes: Enabling low-cost access to high-dimensional, geostatistical-based decision-support analyses

Computer models of environmental systems routinely inform decision making for water resource management. In this context, quantifying uncertainty in the important simulated outputs, and reducing uncertainty through assimilating historic system-state observations, is as important as the numerical model. However, implementing high-dimensional and stochastic workflows are challenging, often requiring that practitioners have theoretical and practical understanding of several advanced topics. Worse, implementing these important analyses can take substantial time and effort. This additional effort is often cited as justification for postponing, or even forgoing, these analyses. Herein, we present scripting tools to facilitate the efficient and repeatable construction of high-dimensional, geostatistical-based PEST interfaces, including uncertainty analyses. As demonstrated, these tools can be applied with minimal effort to a model with varied temporal and spatial discretization. Ultimately, these tools can enable low-cost access to valuable decision-support analyses earlier and more frequently during the modeling workflow.

Environmental Modelling & Software↗
USGS2020

Brightness of the night sky affects loggerhead (Caretta caretta) sea turtle hatchling misorientation but not nest site selection

Sea turtles in the Gulf of Mexico, which are listed as either threatened or endangered under the US Endangered Species Act, face numerous threats from many sources but are particularly susceptible to the effects of light pollution on nesting beaches. Light pollution affects the distribution, density, and placement of nests on beaches, and disrupts seafinding in hatchlings emerging from nests – often leading to their death. Rapid urban growth near Gulf Islands National Seashore (GUIS) over the last century has contributed to increased light pollution on its beaches, and there is concern that light pollution is causing females to lay nests in at-risk locations subject to erosion and flooding and is causing the observed high rates of hatchling misorientation. In this study our objectives were to measure brightness of the nighttime sky (and other variables) at GUIS at loggerhead (Caretta caretta) nests at the time of laying and hatching, and to assess the effects of brightness on the laying of at-risk nests and hatchling misorientation. In addition, we quantified the effects of relocating at-risk nests on nesting success. We found that the contrast in brightness between the landward and seaward directions at GUIS is at least partially responsible for high rates of hatchling misorientation, and we found a strong moderating influence of lunar fraction and lunar altitude on hatchling misorientation (larger lunar fractions and lower lunar altitudes reduced misorientation). We were unable to document any effects of artificial light, lunar fraction, or horizon altitude on the propensity of loggerheads to lay nests in at risk locations; and we found no evidence that relocating nests at GUIS reduced loggerhead nesting success. Rather, we found nesting success and hatchling misorientation rates were improved for relocated loggerhead nests.

Florida↗
USGS2017

Sea turtles, light pollution, and citizen science: A preliminary report

Sea turtles are an important ecological resource for Gulf Islands National Seashore’s (Gulf Islands) waters and shorelines. Regionally, sea turtles face anthropogenic threats from situations such as entanglement in fishing gear and ingestion of marine debris, as well as possible changes in sex ratios due to increasing temperatures related to human-induced global warming. Locally, light pollution from residential, commercial, and industrial neighborhoods from nearby cities impacts the entirety of Gulf Islands, which spans 160 miles along the Gulf Coast, from Florida to Mississippi, and includes critical habitat for threatened and endangered sea turtles. Because light pollution has been hypothesized to negatively impact sea turtle nesting and hatchling survival, Gulf Islands undertook an effort to understand the relationship between light pollution and sea turtles and create unique educational and outreach opportunities by launching a citizen science program called Turtle Teens Helping in the Seashore (Turtle THIS). At the onset, the Turtle THIS program had two primary goals: quantify the association between light pollution and sea turtle nesting and hatching events using rigorous scientific methods; and initiate a citizen science volunteer program to provide youth with hands-on science and environmental stewardship roles, where they also gain employable skills and career opportunities. With multiple scientific hypotheses to consider, the development of a citizen science program became crucial. Such circumstances allowed Turtle THIS to grow a volunteer and intern program, quantify hypothesized light effects on sea turtles through developed methods, and begin to gather preliminary findings.

Florida↗