Geology topics

Fred Johnson

Publications and source records attributed to Fred Johnson.

11 recordsLinked to original sources

USGS2025

Tracking the sources of metals to the San Juan River, Four Corners Region, USA: An introduction to the thematic issue

Surface water quantity and quality is important for arid and semi-arid regions where many people, including underserved and Indigenous communities, rely on a scarce resource for drinking water, irrigation, livestock and ceremonial uses. The southwestern United States, and specifically the Four Corners Region (Colorado, Arizona, New Mexico and Utah), is an example of this situation. Elevated concentrations of metals including aluminium, arsenic and lead were identified in previous studies and this study in the San Juan River from below the Navajo Dam, through the Navajo Nation to Mexican Hat, Utah. An interdisciplinary team applied approaches and principles of geology, geochemistry, geomorphology, hydrology and statistics to gain a better understanding of the tributaries supplying the source(s) of metals to the San Juan River. This introductory paper provides an overview of the ‘Metal geochemical fingerprinting to identify sub-watershed source contributions to surface water at a regional arid watershed scale, Four Corners Region, USA’ thematic collection . An overview of sampling sites, techniques and potential sources of metals is provided. Approaches used in this study could be applied to investigations in similar systems globally.

Four Corners region↗

USGS2025

Quantifying the relative importance of survival threats to a long-lived reptile using expert elicitation

Long-term survival of a conservation-reliant species requires understanding the impact of threats on population growth rate and the management actions that can help mitigate these threats. We used a threat assessment with expert-elicited estimates to determine the relative effect of each stage-specific threat on the population growth rate of the wood turtle Glyptemys insculpta . In addition, we offered potential management actions that could mitigate these threats and examined the relative cost and benefit of each. The experts responded that predators had the largest effect on hatchling and juvenile survival and that road mortality had the largest effect on adult survival. The population growth rate of the simulated turtle population increased the most when predators were removed from the system, though the population trajectory remained negative. Finally, we found that predator control had the lowest cost:benefit ratio of the proposed management actions. The process used in this analysis of expert elicitation combined with modeling that accounts for uncertainty proved to be a useful technique that is less expensive and labor intensive than empirical studies and quicker to implement, although it relies on sufficient empirical studies to inform expert responses. This process could be replicated for other species to inform species status assessments.

Endangered Species Research↗

USGS2022

Open removal models with temporary emigration and population dynamics to inform invasive animal management

Removal sampling data are the primary source of monitoring information for many populations (e.g., invasive species, fisheries). Population dynamics, temporary emigration, and imperfect detection are common sources of variation in monitoring data and are key parameters for informing management. We developed two open robust-design removal models for simultaneously modeling population dynamics, temporary emigration, and imperfect detection: a random walk linear trend model (estimable without ancillary information), and a 2-age class informed population model (InfoPM, closely related to integrated population models) that incorporated prior information for age-structured vital rates and relative juvenile availability. We applied both models to multiyear, removal trapping time-series of a large invasive lizard (Argentine black and white tegu, Salvator merianae ) in three management areas of South Florida to evaluate the effectiveness of management programs. Although estimates of the two models were similar, the InfoPMs generally returned more precise estimates, partitioned dynamics into births, deaths, net migration, and provided a decision support tool to predict population dynamics under different effort scenarios while accounting for uncertainty. Trends in tegu superpopulation abundance estimates were increasing in two management areas despite generally high removal rates. However, tegu abundance appeared to decline in the Core management area, where trapping density was the highest and immigration the lowest. Finally, comparing abundance predictions of no-removal scenarios to those estimated in each management area suggested significant population reductions due to management. These results suggest that local tegu population control via systematic trapping may be feasible with high enough trap density and limited immigration; and highlights the value of these trapping programs. We provided the first estimates of tegu abundance, capture probabilities, and population dynamics, which is critical for effective management. Furthermore, our models are applicable to a wide range of monitoring programs (e.g., carcass recovery or removal point-counts).

Ecology and Evolution↗

USGS2021

Modeling structural mechanics of oyster reef self-organization including environmental constraints and community interactions

Self-organization is a process of establishing and reinforcing local structures through feedbacks between internal population dynamics and external factors. In reef-building systems, substrate is collectively engineered by individuals that also occupy it and compete for space. Reefs are constrained spatially by the physical environment, and by mortality, which reduces production but exposes substrate for recruits. Reef self-organization therefore depends on efficient balancing of production and occupancy of substrate. To examine this, we develop a three-dimensional individual-based model (IBM) of oyster reef mechanics. Shell substrate is grown by individuals as valves, accumulates at the reef level, and degrades following mortality. Single restoration events and subsequent dynamics are simulated for a case study in South Carolina (USA). Variability in model processes is included on recruitment, spatial environmental constraints, and predation, over multiple independent runs and five predator community scenarios. The main goal for this study is to summarize trends in dynamics that are robust across this uncertainty, and from these generate new hypotheses and predictions for future studies. Simulation results demonstrate three phases following restoration: initial transient dynamics with considerable shell loss, followed by growth and saturation of the live population, and then saturation of settlement habitat several years later. Over half of simulations recoup initial shell losses as populations grow, while others continue in decline. The balance between population density, substrate supporting the reef, and exposed surfaces for settlement is mediated by overall population size and size structure, presence of predators, and relative amounts of live individuals and intact dead shells. The efficiency of settlement substrate production improves through time as population size structure becomes more complex, and the population of dead valves accumulates.

Ecological Modelling↗

USGS2020

Using integrated population models for insights into monitoring programs: An application using pink-footed geese

Development of integrated population models (IPMs) assume the absence of systematic bias in monitoring programs, yet many potential sources of systematic bias in monitoring data exist (e.g., under-counts of abundance). By integrating multiple sources of data, we can assess whether various sources of monitoring data provide consistent inferences about changes in population size and, thus, whether monitoring programs appear unbiased. For the purposes of understanding how IPMs could provide insights for monitoring programs, we used the Svalbard breeding population of pink-footed goose ( Anser brachyrhynchus ) as a case study. The Svalbard pink-footed goose is a well-studied species, the focus of the first adaptive-harvest-management program in Europe, and the subject of a variety of long-term monitoring programs. We examined two formulations of an IPM, but ultimately relied on the one that provided a satisfactory fit to all the available data as based on Chi-squared goodness of fit tests. Our analyses suggest a negative bias in November counts (-20 %), a negative bias in capture-mark-recapture estimates of survival (-3 %), and a negative bias in indices of productivity (-23 %). We offer possible explanations for these biases, whether the degree of bias seems reasonable considering those explanations, and how bias might be investigated directly and ultimately avoided or corrected. Finally, we discuss implications of our work for developing IPMs and associated monitoring programs for managing pink-footed geese and other waterbird species.

Ecological Modelling↗

USGS2019

Spatial conservation planning under uncertainty: Adapting to climate change risks using modern portfolio theory

Climate change and urban growth impact habitats, species, and ecosystem services. To buffer against global change, an established adaptation strategy is designing protected areas to increase representation and complementarity of biodiversity features. Uncertainty regarding the scale and magnitude of landscape change complicates reserve planning and exposes decision makers to risk of failing to meet conservation goals. Conservation planning tends to treat risk as an absolute measure, ignoring the context of the management problem and risk preferences of stakeholders. Application to conservation of risk management theory emphasizes diversification of portfolio of assets, with the goal of reducing the impact of system volatility on investment return. We use principles of Modern Portfolio Theory (MPT), which quantifies risk as the variance and correlation among assets, to formalize diversification as an explicit strategy for managing risk in climate‐driven reserve design. We extend MPT to specify a framework that evaluates multiple conservation objectives, allows decision makers to balance management benefits and risk when preferences are contested or unknown, and includes additional decision options such as parcel divestment when evaluating candidate reserve designs. We apply an efficient search algorithm that optimizes portfolio design for large conservation problems and a game theoretic approach to evaluate portfolio tradeoffs that satisfy decision makers with divergent benefit and risk tolerances, or when a single decision maker cannot resolve their own preferences. Evaluating several risk profiles for a case study in South Carolina, our results suggest that a reserve design may be somewhat robust to differences in risk attitude but that budgets will likely be important determinants of conservation planning strategies, particularly when divestment is considered a viable alternative. We identify a possible fiscal threshold where adequate resources allow protecting a sufficiently diverse portfolio of habitats such that the risk of failing to achieve conservation objectives is considerably lower. For a range of sea‐level rise projections, conversion of habitat to open water (14‐180%) and wetland loss (1‐7%) are unable to be compensated under the current protected network. In contrast, optimal reserve design outcomes are predicted to ameliorate expected losses relative to current and future habitat protected under the existing conservation estate.

South Carolina↗

USGS2019

Multi-species duck harvesting using dynamic programming and multi-criteria decision analysis

1.Multiple species are often exposed to a common hunting season, but harvest and population objectives may not be fully achieved if harvest potential varies among species and/or species abundances are not correlated through time. Our goal was to develop an approach for setting a common hunting season that would recognize heterogeneity in species productivity and would select annual hunting seasons conditioned on the status of individual species. 2.We first used stochastic dynamic programming to generate optimal, state‐dependent harvest strategies for 18 candidate regulatory scenarios. We simulated the performance of these strategies, and then used multi‐criteria decision making to identify preferred regulatory scenarios for duck hunting seasons in the Atlantic Flyway of the U.S. 3.Generally, estimates of annual population size were not correlated among species. Mallards had the highest estimated intrinsic rate of growth, green‐winged teal, wood ducks, and ring‐necked ducks had intermediate values, and goldeneyes were the least productive. Estimated carrying capacity was highest for mallards and lowest for green‐winged teal. 4.Managers had greatest interest in maximizing season length (33%) and aggregate duck abundance (28%), and less interest in maximizing aggregate harvest (19%) and the number of years between a change in hunting season regulations (19%). Several regulatory scenarios provided acceptable tradeoffs among these objectives. 5.Synthesis and applications. Separate hunting seasons for various species of game may be untenable, either due to the added cost and regulatory complexity, or because selective harvesting of stocks may be difficult due to problems in species identification. Rather than averaging species‐specific productivities, or basing hunting seasons on the least (or most) productive species, we describe an approach in which productivity and annual population status are considered explicitly for each species. By combining stochastic dynamic programming with multi‐criteria decision analysis, we can identify a regulatory strategy that can address a diverse set of objectives and explicitly recognize the tradeoffs among them. To meet the Atlantic Flyway's objectives as identified by waterfowl managers, our results suggest a regulatory strategy in which the harvest is targeted at 98% of aggregate maximum sustainable yield, most emphasis is placed on accumulating harvests of mallards and wood ducks, and by using a set of regulatory options that are more conservative than those currently in use.

Journal of Applied Ecology↗

USGS2019

Incorporating uncertainty and risk into decision making to reduce nitrogen inputs to impaired waters

This article aims to understand decision making under uncertainty and risk, with a case study on Cape Cod, Massachusetts. Decision makers need to consider imperfect information on the cost and effectiveness of advanced nitrogen-removing on-site wastewater treatment systems as options to mitigate water quality degradation. Research included modeling nitrogen load reduction to impaired coastal waters from seven treatment system technologies and eliciting expert knowledge on their costs. Predictions of nitrogen load removal and cost for each technology incorporated variation in effectiveness and uncertainty in household water use, costs, and expert confidence in costs. The predictions were evaluated using the Pareto efficiency concept to reveal tradeoffs between cost and effectiveness. The stochastic dominance index was used to identify preferred technologies for risk-averse decision making, assuming no further learning is possible. Lastly, the predictions were combined into a cost-effectiveness metric to estimate the expected payoff of implementing the best treatment system in the face of uncertainty and the expected payoff of learning which treatment systems are most cost-effective over time. The expected value of perfect information was calculated as the difference between the expected payoffs. Three technologies revealed Pareto efficient tradeoffs between cost and effectiveness, whereas one technology was the preferred risk-averse option in the absence of future learning. There was a high expected value of perfect information, which could motivate adaptive management on Cape Cod. This research demonstrated decision analysis methods to guide future research and decision making toward meeting water quality objectives and reducing uncertainty.

Massachusetts↗

USGS2019

Harvest assessment for Taiga bean geese in the Central Management Unit: 2019

In 2016 the European Goose Management International Working Group (EGM IWG) began development of an Adaptive Harvest Management (AHM) program for Taiga Bean Geese. In 2017, the IWG adopted an Interim Harvest Strategy consisting of a constant harvest rate (on adults) of 3% for the Central Management Unit (MU) of Taiga Bean Geese. The interim strategy is intended to provide limited hunting opportunity while rebuilding the population. Based on a January count of 41,927, the harvest quota for the 2019 hunting season is 1,740 Taiga Bean Geese (compared to 2,335 and 1,610 for the 2017 and 2018 seasons, respectively). We emphasize that these quotas include both, harvest during the regular season and derogation shooting. Going forward, we describe how an Integrated Population Model (IPM) will use counts at multiple times during the year, along with other demographic information, to estimate population size (and its precision). The IPM can be used to develop an adaptive harvest strategy if unambiguous management objectives can be agreed upon. We provide some initial guidance for formulating those objectives.

Conference Paper↗

USGS2019

Adaptive harvest management for the Svalbard population of pink‐footed geese: 2019 progress summary

This report describes an Adaptive Harvest Management (AHM) program designed to maintain the Svalbard population of Pink-footed Geese (Anser brachyrhynchus) near their target level (60,000) by providing sustainable harvests in Norway and Denmark. Specifically, this report provides recent monitoring and assessment results and their implications for the 2019 hunting season. In this report we provide results for both the set of nine discrete population models used since 2013 and for a recently developed Integrated Population Model (IPM). Updating model weights (set of nine models) and model parameters (IPM) use the most recent monitoring information available (i.e. through spring 2019). We also compare optimal harvest strategies based on the two modeling approaches. Of growing concern, however, has been the observation that the predictive ability of the original population models has declined over time. Going forward, we suggest that use of the IPM is a superior modeling approach for setting hunting seasons for Pink-footed Geese. Population sizes in May as estimated by the IPM are in general agreement with counts and capture-markrecapture estimates but are more precise. Estimates of survival from natural causes were relatively constant over time with a mean of 0.93 and estimates of adult kill rate ranged from 0.04 to 0.13. Estimates of countryspecific kill rates suggest that most of the overall increase in kill rate in recent years is attributed to increasing harvest pressure in Denmark. With respect to productivity, the IPM produced estimates of preseason age ratio that were variable over time, but on average suggested that young constituted about 18% of the population just prior to the hunting season. Estimates of the preseason age ratio correlated well with the number of days above freezing in May in Svalbard. Our results suggest that population size has stabilized because of declining survival rates that have accompanied an increase in kill rates. Adoption of the IPM this year would result in a harvest quota for the 2019 hunting season of 22,000, based on a May population estimate of 76,500 (95% CI: 66,800 – 86,600) and 8 days above freezing in Svalbard. Using the agreed upon allocation of the total allowable harvest, the quotas are 6,600 for Norway and 15,400 for Denmark. Assuming the total quota is met, the IPM predicts a population size in May 2020 of 64,900 (95% CI: 50,300 – 83,700). We note that the updated set of original models used since 2013 suggests an allowable harvest of 40,000 in 2019 based on a November 2018 count of 91,900 geese (comprised of 12,900 young and 79,000 adults). We are skeptical of this large quota, however. Using all available demographic data suggests that the November 2018 population size was lower than that indicated by the count and that the winter population experienced more natural mortality than expected. These insights demonstrate the inherent risk of basing a harvest quota on a single population count, irrespective of other demographic data.

Report↗

USGS2014

Integrating land cover modeling and adaptive management to conserve endangered species and reduce catastrophic fire risk

Land cover modeling is used to inform land management, but most often via a two-step process, where science informs how management alternatives can influence resources, and then, decision makers can use this information to make decisions. A more efficient process is to directly integrate science and decision-making, where science allows us to learn in order to better accomplish management objectives and is developed to address specific decisions. Co-development of management and science is especially productive when decisions are complicated by multiple objectives and impeded by uncertainty. Multiple objectives can be met by the specification of tradeoffs, and relevant uncertainty can be addressed through targeted science (i.e., models and monitoring). We describe how to integrate habitat and fuel monitoring with decision-making focused on the dual objectives of managing for endangered species and minimizing catastrophic fire risk. Under certain conditions, both objectives might be achieved by a similar management policy; other conditions require tradeoffs between objectives. Knowledge about system responses to actions can be informed by developing hypotheses based on ideas about fire behavior and then applying competing management actions to different land units in the same system state. Monitoring and management integration is important to optimize state-specific management decisions and to increase knowledge about system responses. We believe this approach has broad utility and identifies a clear role for land cover modeling programs intended to inform decision-making.

Land↗