Search USGS⌕ Search

SEARCH · Search USGS

Results for “Journal of Applied Probability”

Search indexed USGS publications on groundwater, aquifers, geologic maps, mineral resources and earthquakes. Explore source records by subject and place.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 127 records · Page 7Linked to original sources
USGS2013

Correcting length-frequency distributions for imperfect detection

Sampling gear selects for specific sizes of fish, which may bias length-frequency distributions that are commonly used to assess population size structure, recruitment patterns, growth, and survival. To properly correct for sampling biases caused by gear and other sources, length-frequency distributions need to be corrected for imperfect detection. We describe a method for adjusting length-frequency distributions when capture and recapture probabilities are a function of fish length, temporal variation, and capture history. The method is applied to a study involving the removal of Smallmouth Bass Micropterus dolomieu by boat electrofishing from a 38.6-km reach on the Yampa River, Colorado. Smallmouth Bass longer than 100 mm were marked and released alive from 2005 to 2010 on one or more electrofishing passes and removed on all other passes from the population. Using the Huggins mark–recapture model, we detected a significant effect of fish total length, previous capture history (behavior), year, pass, year×behavior, and year×pass on capture and recapture probabilities. We demonstrate how to partition the Huggins estimate of abundance into length frequencies to correct for these effects. Uncorrected length frequencies of fish removed from Little Yampa Canyon were negatively biased in every year by as much as 88% relative to mark–recapture estimates for the smallest length-class in our analysis (100–110 mm). Bias declined but remained high even for adult length-classes (≥200 mm). The pattern of bias across length-classes was variable across years. The percentage of unadjusted counts that were below the lower 95% confidence interval from our adjusted length-frequency estimates were 95, 89, 84, 78, 81, and 92% from 2005 to 2010, respectively. Length-frequency distributions are widely used in fisheries science and management. Our simple method for correcting length-frequency estimates for imperfect detection could be widely applied when mark–recapture data are available.

Colorado↗
USGS1991

Rock chemistry and fluid inclusion studies as exploration tools for ore deposits in the Sila batholith, southern Italy

The Sila batholith is the focus of an extensive petrogenetic research program, which includes an assessment of its potential to host granite-related ore deposits. Univariate and multivariate statistical techniques were applied to major- and minor-element rock geochemical data. The analysis indicates that the highest potential for mineralization occurs in corundum-normative, peraluminous, unfoliated, relatively late-stage plutons. The plutons are enriched in Rb, Nb, Ta and U, but depleted in Fe, Mg and Sr. The K/Rb, Ba/Rb, Rb/Sr and Rb 3 /Ba·Sr·K indices and high R -factor scores of Si-K-Rb are typical of mineralized granitic rocks. A reconnaissance fluid inclusion study indicates that the sub-solidus rock was infiltrated by solutions of widely different temperatures (50–416°C) and variable salinities (0 to ∼26 wt.% NaCl equivalent). The higher-temperature solutions probably represent granite or magmatic-related Hercynian fluids, whereas the lower-temperature fluids may be either Hercynian or Alpine in age. Fluids with characteristics typical of mineralized “porphyry” systems have not been recognized.

Journal of Geochemical Exploration↗
USGS2017

Are ranger patrols effective in reducing poaching-related threats within protected areas?

Poaching is one of the greatest threats to wildlife conservation world-wide. However, the spatial and temporal patterns of poaching activities within protected areas, and the effectiveness of ranger patrols and ranger posts in mitigating these threats, are relatively unknown. We used 10 years (2006–2015) of ranger-based monitoring data and dynamic multi-season occupancy models to quantify poaching-related threats, to examine factors influencing the spatio-temporal dynamics of these threats and to test the efficiency of management actions to combat poaching in Nyungwe National Park (NNP), Rwanda. The probability of occurrence of poaching-related threats was highest at lower elevations (1,801–2,200 m), especially in areas that were close to roads and tourist trails; conversely, occurrence probability was lowest at high elevation sites (2,601–3,000 m), and near the park boundary and ranger posts. The number of ranger patrols substantially increased the probability that poaching-related threats disappear at a site if threats were originally present (i.e. probability of extinction of threats). Without ranger visits, the annual probability of extinction of poaching-related threats was an estimated 7%; this probability would increase to 20% and 57% with 20 and 50 ranger visits per year, respectively. Our results suggest that poaching-related threats can be effectively reduced in NNP by adding ranger posts in areas where they do not currently exist, and by increasing the number of patrols to sites where the probability of poaching activities is high. Synthesis and applications . Our application of dynamic occupancy models to predict the probability of presence of poaching-related threats is novel, and explicitly considers imperfect detection of illegal activities. Based on the modelled relationships, we identify areas that are most vulnerable to poaching, and offer insights regarding how ranger patrols can be optimally deployed to reduce poaching-related threats and other illegal activites, while taking into account potential sampling biases. We show that poaching can be effectively reduced by increasing ranger patrols to areas under high risk of poaching activities, and by adding ranger patrols near these sites. These findings are broadly applicable to national parks and protected areas experiencing a high degree of poaching and other illegal activities.

Nyungwe National Park↗
USGS1987

Probability plotting position formulas for flood records with historical information

For purposes of evaluating fitted flood frequency distributions or for purposes of estimating distributions directly from plots of flood peaks versus exceedance probabilities (either by subjective or objective techniques), one needs a probability plotting position formula which can be applied to all of the flood data available: both systematic and historic floods. Some of the formulas in use are simply extensions of existing formulas (such as Hazen and Weibull) used on systematic flood records. New plotting position formulas proposed by Hirsch and Stedinger (1986) and in this paper are based on a recognition that the flood data arises from partially censored sampling of the flood record. The theoretical appropriateness, bias in probability and bias in discharge of the various plotting position formulas are considered. The methods are compared in terms of their effects on flood frequency estimation when an objective curve-fitting method of estimation is employed. Consideration is also given to the correct interpretation of the historical record length and the effect of incorrectly assuming that record length equals the time since the first known historical flood. This assumption is employed in many flood frequency studies and may result in a substantial bias in estimated design flood magnitudes.

Journal of Hydrology↗
USGS2013

Demographic variation, reintroduction, and persistence of an island duck ( Anas laysanensis )

Population variation in life history can be important for predicting successful establishment and persistence of reintroduced populations of endangered species. The Laysan duck (Anas laysanensis) is an endangered bird native to the Hawaiian Archipelago that was extirpated from most islands after the introduction of mammalian predators. Laysan ducks were restricted to a single remote island, Laysan Island (4.1 km 2 ), for nearly 150 years. Since the species is not known to disperse between distant Hawaiian Islands today, 42 wild birds from Laysan Island were translocated to another mammalian predator-free low-lying atoll (Midway Atoll; 6.0 km 2 ) to reduce extinction risk. We explored how variation in demography influences establishment and longer-term retention of genetic diversity (rare alleles) for reintroductions of this species. We observed dramatic differences in population growth between the source (λ = 1.18) and reintroduced (λ = 3.28) population. The number of eggs hatched at Midway Atoll was greater than at Laysan Island, however, we found no difference in hatching success (proportion of clutch hatched) between populations. Adult females produced 3 times as many fledglings per breeding year on Midway Atoll compared to Laysan Island. We estimated population abundance of both populations until 2010 and applied a Gompertz model with a Bayesian approach to infer density dependence, process variation, observation error, and carrying capacity for the Laysan Island and Midway Atoll populations. The carrying capacity from the Gompertz model for Midway Atoll (K = 883 ± 210 SD) was estimated to be greater than that of Laysan Island (K = 598 ± 76 SD). Translocations with small numbers of founders and no immigration can create population bottlenecks, leading to loss of genetic variation over time, and potentially reducing the reintroduced population's viability or its potential to serve as a source for future translocations. Therefore, we also assessed the probability of retaining rare alleles in an isolated reintroduced Laysan duck population using life history parameters observed from the Laysan Island and Midway Atoll populations; we concluded that additional founders are needed under scenarios using demographic estimates from both Laysan Island and Midway Atoll to retain either 90% or 95% of source population genetic diversity.

Laysan Island;Midway Atoll↗
USGS2025

Evaluation of solid bitumen created from marine oil shale bituminite under hydrous and anhydrous pyrolysis conditions

To test the influence of environmental conditions on aromaticity of solid bitumen generated during petroleum generation, four organic-rich (26–36 wt% total organic carbon) oil shale samples collected from the Neoproterozoic–Lower Cambrian restricted marine Salt Range Formation in the upper Indus Basin, Pakistan, were pyrolyzed under hydrous and anhydrous conditions. Experiments used closed system batch reactors at subcritical water temperatures between 300 and 370°C for 72 h. Thermal conversion of bituminite in the Salt Range oil shales created a newly formed solid bitumen, similar to previous observations from experiments on the Eocene lacustrine Green River Formation Mahogany zone oil shale. Newly formed solid bitumen in the Salt Range Formation oil shales generally has higher reflectance (R o ) in hydrous residues compared to anhydrous experiments subjected to the same time-temperature conditions, also similar to prior observations. This finding supports the hypothesis that radical disproportionation is favored in the presence of hydrogen contributed by water, promoting aromatization in the solid bitumen residue with concomitant increased expulsion of saturated hydrocarbons. Indigenous solid bitumen (and vitrinite in a comparison sample) also shows higher reflectance values in hydrous versus anhydrous residues, indicating that additional aromatization in the presence of exogenous hydrogen occurs both in newly formed organic matter and in organic matter that is present throughout the experiment. Despite similarities in their bulk rock geochemical screening parameters, R o evolution shows different trajectories amongst the four Salt Range oil shales, suggesting as-yet undetermined differences in kinetic properties which are probably related to differences in a priori chemical composition. These results have implications for the use of solid bitumen reflectance (BR o ) as a thermal proxy, suggesting BR o values and appearance could vary as a function of the concentration of water. Variation in water concentration may be present at the reservoir or formation scale, but may also be present at a much finer scale in tight oil shales where permeability is several orders of magnitude lower than conventional reservoirs. Therefore, local variations in the presence of water potentially could explain substantial variation in BR o values and appearance in closely spaced source rock samples and even within an individual microscope field.

Journal of Analytical and Applied Pyrolysis↗
USGS2018

Distance models as a tool for modelling detection probability and density of native bumblebees

Effective monitoring of native bee populations requires accurate estimates of population size and relative abundance among habitats. Current bee survey methods, such as netting or pan trapping, may be adequate for a variety of study objectives but are limited by a failure to account for imperfect detection. Biases due to imperfect detection could result in inaccurate abundance estimates or erroneous insights about the response of bees to different environments. To gauge the potential biases of currently employed survey methods, we compared abundance estimates of bumblebees ( Bombus spp.) derived from hierarchical distance sampling models (HDS) to bumblebee counts collected from fixed‐area net surveys (“net counts”) and fixed‐width transect counts (“transect counts”) at 47 early‐successional forest patches in Pennsylvania. Our HDS models indicated that detection probabilities of Bombus spp. were imperfect and varied with survey‐ and site‐covariates. Despite being conspicuous, Bombus spp. were not reliably detected beyond 5 m. Habitat associations of Bombus spp. density were similar across methods, but the strength of association with shrub cover differed between HDS and net counts. Additionally, net counts suggested sites with more grass hosted higher Bombus spp. densities whereas HDS suggested that grass cover was associated with higher detection probability but not Bombus spp. density. Density estimates generated from net counts and transect counts were 80%–89% lower than estimates generated from distance sampling. Our findings suggest that distance modelling provides a reliable method to assess Bombus spp. density and habitat associations, while accounting for imperfect detection caused by distance from observer, vegetation structure, and survey covariates. However, detection/non‐detection data collected via point‐counts, line‐transects and distance sampling for Bombus spp. are unlikely to yield species‐specific density estimates unless individuals can be identified by sight, without capture. Our results will be useful for informing the design of monitoring programs for Bombus spp. and other pollinators.

Pennsylvania↗
USGS2021

Assessment and significance of the frequency domain for trends in annual peak streamflow

Risk management of nonstationary floods depends on an understanding of trends over a range of flood frequencies representing small (frequent) to large (infrequent) floods. Quantile regression is applied to the annual peak streamflow distributions at 2683 sites in the contiguous United States to test for trends in the 10th quantile (floods with a 0.9 annual exceedance probability), the 50th quantile (median annual flood), and 90th quantile (floods with a 0.1 annual exceedance probability). Trends are most common (36% of sites) for the median annual flood (50th quantile) and often coherent with trends in both frequent small floods (10th quantile) and infrequent large floods (90th quantile). Changes in the at-site variance of annual peak streamflow, indicated by convergence (decreasing variance) or divergence (increasing variance) of the 10th and 90th quantiles over time, are primarily in response to reservoir operation or urban development rather than climate. An analysis of synthetic series generated from nonstationary distributions demonstrates that quantile regression and standard trend tests used in flood frequency analysis have limited power and high rates of false negatives (>70%) when a test has a significance of p = 0.05. Quantile regression and tests with lower significance complement standard trend testing to inform flood risk management.

Journal of Flood Risk Management↗
USGS2022

Towards real-time probabilistic ash deposition forecasting for New Zealand

Volcanic ashfall forecasts are highly dependent on eruption source parameters (ESPs) and synoptic weather conditions at the time and location of the eruption. In New Zealand, MetService and GNS Science have been jointly developing an ashfall forecast system that incorporates four-dimensional high-resolution numerical weather prediction (NWP) and ESPs into the HYSPLIT model, a state-of-the art hybrid Eulerian and Lagrangian dispersion model widely used for volcanic ash. However, these forecasts are based on discrete ESPs combined with a deterministic weather forecast and thus provide no information on output uncertainty. This shortcoming hinders stakeholder decision making, particularly near the geographical margin of forecasted ashfall and in areas with large gradients in forecasted ash deposition. Our study presents a new approach that incorporates uncertainty from both eruptive and meteorological inputs to deliver uncertainty in the model output. To this end, we developed probability density functions (PDFs) for three key ESPs (plume height, mass eruption rate, eruption duration) tailored to New Zealand’s volcanoes and combine them with NWP ensemble datasets to generate probabilistic ashfall forecasts using the HYSPLIT model. We show that the Latin Hypercube Sampling (LHS) technique can be used to representatively span this four-dimensional parameter space and allow us to add uncertainty quantification to rapid response forecast systems. For a case study of a hypothetical eruption at Tongariro, New Zealand we suggest that large parts of New Zealand’s North Island would not receive adequate warning for potential ashfall if uncertainties were not included in the forecasts. We also propose new probabilistic summary products to support public information and emergency responders decision making.

Journal of Applied Volcanology↗
USGS2021

Human activities and weather drive contact rates of wintering elk

Wildlife aggregation patterns can influence disease transmission. However, limited research evaluates the influence of anthropogenic and natural factors on aggregation. Many managers would like to reduce wildlife contact rates, driven by aggregation, to limit disease transmission. We develop a novel analytical framework to quantify how management activities such as supplemental feeding and hunting versus weather drive contact rates while accounting for correlated contacts. We apply the framework to the National Elk Refuge (NER), Wyoming, USA, where the probable arrival of chronic wasting disease (CWD) has magnified concerns. We used a daily proximity index to measure contact rates among 68 global positioning system collared elk from 2016 to 2019. We modelled contact rates as a function of abiotic weather‐related effects, anthropogenic effects and aggregation from the prior day. The winter of 2017–2018 had greater natural forage availability and little snow, which led to a rare non‐feeding year on the NER and provided a unique opportunity to evaluate the effect of feeding on contact rates relative to other conditions. Supplemental feeding was the strongest predictor of aggregation, and contact rates were 2.6 times larger while feeding occurred compared to the baseline rate (0.34 and 0.13, respectively). Snow‐covered area was the second strongest predictor of contact rates highlighting the importance of abiotic factors to elk aggregation, but this effect had half the strength of feeding. These results are the first to show, even in animals that congregate naturally, how greatly supplemental feeding amplifies aggregation. Contact rates were also 23% lower during times when elk hunting was active (0.10) compared to the baseline. Synthesis and applications . Supplemental feeding increased contacts between elk well above the natural effects of weather, even after accounting for correlated movement expected in wintering ungulates. Similarly, differences in hunting season timing with adjacent areas led to an increase in contacts, suggesting an additional management option for reducing aggregation. The analytical framework presented supports the evaluation of temporally varying management actions that influence aggregation broadly and can be easily implemented whether the interest in changing aggregation is related to reduction of disease transmission, human–wildlife conflict or inter‐species competition.

Wyoming↗
USGS2016

A goodness-of-fit test for occupancy models with correlated within-season revisits

Occupancy modeling is important for exploring species distribution patterns and for conservation monitoring. Within this framework, explicit attention is given to species detection probabilities estimated from replicate surveys to sample units. A central assumption is that replicate surveys are independent Bernoulli trials, but this assumption becomes untenable when ecologists serially deploy remote cameras and acoustic recording devices over days and weeks to survey rare and elusive animals. Proposed solutions involve modifying the detection-level component of the model (e.g., first-order Markov covariate). Evaluating whether a model sufficiently accounts for correlation is imperative, but clear guidance for practitioners is lacking. Currently, an omnibus goodnessof- fit test using a chi-square discrepancy measure on unique detection histories is available for occupancy models (MacKenzie and Bailey, Journal of Agricultural, Biological, and Environmental Statistics, 9, 2004, 300; hereafter, MacKenzie– Bailey test). We propose a join count summary measure adapted from spatial statistics to directly assess correlation after fitting a model. We motivate our work with a dataset of multinight bat call recordings from a pilot study for the North American Bat Monitoring Program. We found in simulations that our join count test was more reliable than the MacKenzie–Bailey test for detecting inadequacy of a model that assumed independence, particularly when serial correlation was low to moderate. A model that included a Markov-structured detection-level covariate produced unbiased occupancy estimates except in the presence of strong serial correlation and a revisit design consisting only of temporal replicates. When applied to two common bat species, our approach illustrates that sophisticated models do not guarantee adequate fit to real data, underscoring the importance of model assessment. Our join count test provides a widely applicable goodness-of-fit test and specifically evaluates occupancy model lack of fit related to correlation among detections within a sample unit. Our diagnostic tool is available for practitioners that serially deploy survey equipment as a way to achieve cost savings.

Ecology and Evolution↗
USGS2024

Cross-fade sampling: Extremely efficient Bayesian inversion for a variety of geophysical problems

This paper introduces cross-fade sampling, a computationally efficient Markov Chain Monte Carlo simulation method that uses a semi-analytical approach to quickly solve Bayesian inverse problems that do not themselves have an analytical solution. Cross-fading is efficient in two ways. First, it requires fewer samples to obtain the same quality simulation of the target probability density function (PDF). Secondly, it is much faster to evaluate the posterior probability of each sample than conventional sampling methods for simulating Bayesian posterior PDFs. Conventional methods require evaluating the prior probability (which describes your a priori constraints) and data likelihood (which describes the fit between the observations and the predictions of the model) for each sample model. However, cross-fading does not require evaluating the data likelihood, meaning that ‘big data’ can be fit with zero additional computational cost. Further, the cross-fading approach can be used to calculate the marginal likelihood associated with a model design, facilitating model comparison and Bayesian model averaging. Topics covered in this paper include derivation of the cross-fade approach and how it can be used to simulate Bayesian posterior PDFs and compute the marginal likelihood, discussion of the class of problems to which cross-fading can be applied (with examples from earthquake statistics, earthquake ground motion modelling, volcanic eruption forecasting, and finite fault slip modelling), demonstration of efficiency relative to existing sampling methods and discussion of how cross-fading can be used to account for prediction errors (i.e. epistemic errors) as part of the geophysical inverse problem.

Geophysical Journal International↗
USGS2025

Aggregating three sources of long-term trends of swallows and martins to identify priority conservation areas in the Great Lakes region

1. Long-term monitoring of bird populations across scales is important in evaluating conservation targets and creating effective conservation strategies. For nearly six decades, the Breeding Bird Survey (BBS) has served as the primary broad-scaled source of relative abundance trends of swallows and martins in North America. Recently, however, it has become possible to obtain breeding population trends using semi-structured eBird community science data. Moreover, weather surveil-lance radar data of swallow and martin roosting populations yield a third complementary source of trend information. 2. Using results from these three approaches, we propose a novel method of spatially combining estimates of percent change per year into a probability of directional agreement and/or disagreement that describes (1) the direction of the trend within a given region, (2) the amount of evidence associated with the estimate and (3) how much uncertainty surrounds it. We focus our efforts on an area of high Hirundinidae concentration in the North American Great Lakes region and predict trends from 2012 to 2022. 3. We found a high probability of agreement between all three sources about ob-served declines in swallow and martin trends in the region surrounding Lake Ontario and to the west of Lake Michigan. Focusing future research on these regions could improve our understanding of these declines and help build more targeted conservation initiatives. 4. Synthesis and applications. Our data integration methodology allows managers to identify regions that accumulate evidence of concerning trends across multiple wildlife monitoring schemes. These regions can thus be prioritized in conservation and management efforts. This approach can be generalized to other sources of long-term monitoring data of different species, at different stages of their annual cycle, in any geographic location.

Great Lakes region↗
USGS2022

Identifying and characterizing juvenile lake sturgeon (Acipenser fulvescens, Rafinesque, 1817) occupancy hot spots within the St. Clair-Detroit River System

Over the past two decades, extensive monitoring has been conducted in the St. Clair – Detroit River System to describe spatial and temporal patterns of lake sturgeon ( Acipenser fulvescens ). To characterize spatial patterns in juvenile lake sturgeon (<1000 mm TL) based on survey collections, ‘hot spots’ were identified through optimized hot spot analysis (HSA). This HSA was then interpolated by inverse distance weighted analysis to determine extent of identified ‘hot spots’ and ‘cold spots’. Additionally, habitat variables (i.e., water depth, water velocity, and dominant substrate type) were investigated using a single season occupancy model to determine their influence on juvenile lake sturgeon occupancy probability. In total, 1203 juvenile lake sturgeon were captured across 4197 surveys. Three unique ‘hot spots’ were identified; western Lake Erie, Fighting Island in the Detroit River, and the North Channel in the St. Clair River. Interpolated ‘hot spots’ encompassed 73.1 km² in western Lake Erie, 4.7 km² near Fighting Island, and 6.6 km² in the North Channel. Detection probabilities within ‘hot spots’ ranged from 8.8%–43.4%. No habitat variables significantly predicted juvenile lake sturgeon occupancy. Juvenile lake sturgeon were captured in western Lake Erie where the water depth was >5.1 m and odds of occupancy increased with increased water velocity. Juvenile lake sturgeon in the Detroit and St. Clair River ‘hot spots’ were captured at sites with mean benthic water velocities ranging from 0.20–0.60 m/s and where water depth was >7.3 m. Irrespective of waterbody, 69% of all juveniles were detected over dominant sand and gravel substrates. These results provide valuable insight about juvenile habitat use that can help managers formulate effective conservation and restoration strategies supporting the continued recovery of Great Lakes lake sturgeon.

Michigan↗
USGS2018

N-mix for fish: estimating riverine salmonid habitat selection via N-mixture models

Models that formulate mathematical linkages between fish use and habitat characteristics are applied for many purposes. For riverine fish, these linkages are often cast as resource selection functions with variables including depth and velocity of water and distance to nearest cover. Ecologists are now recognizing the role that detection plays in observing organisms, and failure to account for imperfect detection can lead to spurious inference. Herein, we present a flexible N-mixture model to associate habitat characteristics with the abundance of riverine salmonids that simultaneously estimates detection probability. Our formulation has the added benefits of accounting for demographics variation and can generate probabilistic statements regarding intensity of habitat use. In addition to the conceptual benefits, model application to data from the Trinity River, California, yields interesting results. Detection was estimated to vary among surveyors, but there was little spatial or temporal variation. Additionally, a weaker effect of water depth on resource selection is estimated than that reported by previous studies not accounting for detection probability. N-mixture models show great promise for applications to riverine resource selection.

Canadian Journal of Fisheries and Aquatic Sciences↗
USGS2020

Machine-learning models to map pH and redox conditions in groundwater in a layered aquifer system, Northern Atlantic Coastal Plain, eastern USA

Study region The study was conducted in the Northern Atlantic Coastal Plain aquifer system, in the eastern USA. Study focus Groundwater pH and redox conditions are fundamental chemical characteristics controlling the distribution of many contaminants of concern for drinking water or the ecological health of receiving waters. In this study, pH and redox conditions were modeled and mapped in a complex, layered aquifer system. Machine-learning methods (boosted regression trees) were applied to data from 3000 to 5000 wells. Predicted pH and the probability of anoxic conditions, defined by three thresholds of dissolved oxygen (0.5, 1, and 2 mg/L), were mapped at the 1-km 2 scale for each of 10 regional aquifer layers. New Hydrological Insights for the Region Maps depict the extent of acidic groundwater and oxic conditions in the shallow, unconfined surficial aquifer and in unconfined, recharge-proximal areas of underlying aquifers, in contrast to alkaline and anoxic groundwater elsewhere. Geographic patterns and influential predictors–including elevation, overlying confining-units thickness, and simulated groundwater age and flux–are consistent with prior understanding of the processes controlling pH and redox in the aquifer system. The model-based maps support robust estimates of aquifer proportions, either areal or volumetric, likely to contain groundwater of a specified quality or be vulnerable to specific pH- or redox-sensitive contaminants. The machine-learning methods were an effective tool to map groundwater quality at the regional scale.

New Jersey, Maryland, Delaware, Virginia↗
USGS2018

Wanted dead or alive: A state-space mark-recapture-recovery model incorporating multiple recovery types and state uncertainty

We developed a state-space mark-recapture-recovery model that incorporates multiple recovery types and state uncertainty to estimate survival of an anadromous fish species. We apply the model to a dataset of out-migrating juvenile steelhead trout (Oncorhynchus mykiss) tagged with passive integrated transponders, recaptured during outmigration, and recovered on bird colonies in the Columbia River basin (2008-2014). Recoveries on bird colonies are often ignored in survival studies because the river reach of mortality is often unknown, which we model as a form of state uncertainty. Median outmigration survival from release to the lower river (river kilometer 729 to 75) ranged from 0.27 to 0.35, depending on year. Recovery probabilities were frequently >0.20 in the first river reach following tagging, indicating that one out of five fish that died in that reach was recovered on a bird colony. Integrating dead recovery data provided increased parameter precision, estimation of where birds consumed fish, and survival estimates across larger spatial scales. More generally, these modeling approaches provide a flexible framework to integrate multiple sources of tag recovery data into mark-recapture studies.

Oregon, Washington↗
USGS2003

Growth rate predicts mortality of Abies concolor in both burned and unburned stands

Tree mortality is often the result of both long-term and short-term stress. Growth rate, an indicator of long-term stress, is often used to estimate probability of death in unburned stands. In contrast, probability of death in burned stands is modeled as a function of short-term disturbance severity. We sought to narrow this conceptual gap by determining ( i ) whether growth rate, in addition to crown scorch, is a predictor of mortality in burned stands and ( ii ) whether a single, simple model could predict tree death in both burned and unburned stands. Observations of 2622 unburned and 688 burned Abies concolor (Gord. & Glend.) Lindl. (white fir) in the Sierra Nevada of California, U.S.A., indicated that growth rate was a significant predictor of mortality in the unburned stands, while both crown scorch and radial growth were significant predictors of mortality in the burned stands. Applying the burned stand model to unburned stands resulted in an overestimation of the unburned stand mortality rate. While failing to create a general model of tree death for A. concolor , our findings underscore the idea that similar processes may affect mortality in disturbed and undisturbed stands.

Canadian Journal of Forest Research↗