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At least 559 records · Page 31Linked to original sources
USGS2017

Basis function models for animal movement

Advances in satellite-based data collection techniques have served as a catalyst for new statistical methodology to analyze these data. In wildlife ecological studies, satellite-based data and methodology have provided a wealth of information about animal space use and the investigation of individual-based animal–environment relationships. With the technology for data collection improving dramatically over time, we are left with massive archives of historical animal telemetry data of varying quality. While many contemporary statistical approaches for inferring movement behavior are specified in discrete time, we develop a flexible continuous-time stochastic integral equation framework that is amenable to reduced-rank second-order covariance parameterizations. We demonstrate how the associated first-order basis functions can be constructed to mimic behavioral characteristics in realistic trajectory processes using telemetry data from mule deer and mountain lion individuals in western North America. Our approach is parallelizable and provides inference for heterogenous trajectories using nonstationary spatial modeling techniques that are feasible for large telemetry datasets. Supplementary materials for this article are available online.

Journal of the American Statistical Association↗
USGS2026

Trends in subdaily to daily rainfall in Florida, 1990–2022

Changing rainfall patterns and intensifying rainfall extremes affect urban infrastructure and can increase flash-flood risk. Understanding how climate change has altered rainfall can support state and local agencies as they adapt and build resiliency. In this study, rainfall data from 23 weather stations in Florida were used to examine temporal and spatial trends over the period 1990–2022. Subdaily to daily rainfall events of durations 1, 2, 3, 6, 12, and 24 h were examined. A variety of statistical methods were applied to examine annual and seasonal trends, including quantile regression, extreme value analysis, run theory using the Mann–Kendall test, Sen–Theil slope, and Poisson and negative binomial tests, and threshold exceedance rates using generalized additive models. Using subdaily rainfall data posed challenges, including equipment failures, limited documentation of the quality assurance and control process, and potential measurement interferences. Results indicated that over 1990–2022, there was a decrease in hourly rainfall extremes but an increase at moderate quantiles. Overall, the number of rainfall events increased, particularly at shorter durations, but the mean total rainfall per event decreased. Additionally, the annual number of daily rainfall extremes showed more decreases than increases.

Florida↗
USGS2012

Fate and transport of cyanobacteria and associated toxins and taste-and-odor compounds from upstream reservoir releases in the Kansas River, Kansas, September and October 2011

Cyanobacteria cause a multitude of water-quality concerns, including the potential to produce toxins and taste-and-odor compounds. Toxins and taste-and-odor compounds may cause substantial economic and public health concerns and are of particular interest in lakes, reservoirs, and rivers that are used for drinking-water supply, recreation, or aquaculture. The Kansas River is a primary source of drinking water for about 800,000 people in northeastern Kansas. Water released from Milford Lake to the Kansas River during a toxic cyanobacterial bloom in late August 2011 prompted concerns about cyanobacteria and associated toxins and taste-and-odor compounds in downstream drinking-water supplies. During September and October 2011 water-quality samples were collected to characterize the transport of cyanobacteria and associated compounds from upstream reservoirs to the Kansas River. This study is one of the first to quantitatively document the transport of cyanobacteria and associated compounds during reservoir releases and improves understanding of the fate and transport of cyanotoxins and taste-and-odor compounds downstream from reservoirs. Milford Lake was the only reservoir in the study area with an ongoing cyanobacterial bloom during reservoir releases. Concentrations of cyanobacteria and associated toxins and taste-and-odor compounds in Milford Lake (upstream from the dam) were not necessarily indicative of outflow conditions (below the dam). Total microcystin concentrations, one of the most commonly occurring cyanobacterial toxins, in Milford Lake were 650 to 7,500 times higher than the Kansas Department of Health and Environment guidance level for a public health warning (20 micrograms per liter) for most of September 2011. By comparison, total microcystin concentrations in the Milford Lake outflow generally were less than 10 percent of the concentrations in surface accumulations, and never exceeded 20 micrograms per liter. The Republican River, downstream from Milford Lake, was the only Kansas River tributary with detectable microcystin concentrations throughout the study period, and concentrations exceeded 1 microgram per liter for most of September 2011. Microcystin was detected periodically in other tributaries, but concentrations were low (less than 0.3 micrograms per liter). In contrast, the taste-and-odor compounds geosmin and 2-methylisoborneol (MIB) were detected in all tributaries located immediately downstream from reservoirs and total concentrations generally exceeded the human detection threshold (5 to 10 nanograms per liter) from September through mid-October. Microcystin, geosmin, and MIB were not detected in the Smoky Hill River upstream from the confluence with the Republican River that forms the Kansas River. Within a week after initial reservoir releases, microcystin, geosmin, and MIB were detected throughout a 173-mile reach of the Kansas River; these compounds remained detectable throughout the reach until mid-October. Losses to groundwater when streamflows in the Kansas River were increasing indicate the potential for reservoir releases to affect groundwater quality as well as surface-water quality. Total microcystin concentrations in the Kansas River generally were highest within about 24 miles of the confluence of the Smoky Hill and Republican Rivers, and decreased downstream; concentrations exceeded 1 microgram per liter in the Kansas River upstream from Topeka during the first 2 weeks of September. Patterns in microcystin occurrence and concentration at Kansas River tributary and main-stem sites indicate that Milford Lake was the source of microcystin in the Kansas River; however, the source of taste-and-odor compounds was not as evident, possibly because multiple tributaries contributed taste-and-odor compounds to the Kansas River. Microcystin and taste-and-odor compounds co-occurred in 56 percent of samples collected, indicating co-occurrence was common. Despite frequent co-occurrence, the spatial and temporal patterns in microcystin, geosmin, and MIB were unique and did not necessarily match patterns in cyanobacterial abundance. Use of a single compound or cyanobacterial abundance alone cannot necessarily be used as an indicator of the presence or concentration of these compounds. Measured concentrations of cyanobacteria and associated compounds were substantially higher than expected concentrations based on simple dilution models at some sites and substantially lower at others, though spatial and temporal patterns were unique for individual compounds. Data were not collected in such a way to determine whether differences between measured and expected concentrations were statistically significant. Results, however, indicate that simple dilution models were not sufficient to describe the downstream transport of cyanobacteria and associated compounds in the Kansas River.

Kansas↗
USGS2015

Simulation of groundwater flow and analysis of the effects of water-management options in the North Platte Natural Resources District, Nebraska

The North Platte Natural Resources District (NPNRD) has been actively collecting data and studying groundwater resources because of concerns about the future availability of the highly inter-connected surface-water and groundwater resources. This report, prepared by the U.S. Geological Survey in cooperation with the North Platte Natural Resources District, describes a groundwater-flow model of the North Platte River valley from Bridgeport, Nebraska, extending west to 6 miles into Wyoming. The model was built to improve the understanding of the interaction of surface-water and groundwater resources, and as an optimization tool, the model is able to analyze the effects of water-management options on the simulated stream base flow of the North Platte River. The groundwater system and related sources and sinks of water were simulated using a newton formulation of the U.S. Geological Survey modular three-dimensional groundwater model, referred to as MODFLOW–NWT, which provided an improved ability to solve nonlinear unconfined aquifer simulations with wetting and drying of cells. Using previously published aquifer-base-altitude contours in conjunction with newer test-hole and geophysical data, a new base-of-aquifer altitude map was generated because of the strong effect of the aquifer-base topography on groundwater-flow direction and magnitude. The largest inflow to groundwater is recharge originating from water leaking from canals, which is much larger than recharge originating from infiltration of precipitation. The largest component of groundwater discharge from the study area is to the North Platte River and its tributaries, with smaller amounts of discharge to evapotranspiration and groundwater withdrawals for irrigation. Recharge from infiltration of precipitation was estimated with a daily soil-water-balance model. Annual recharge from canal seepage was estimated using available records from the Bureau of Reclamation and then modified with canal-seepage potentials estimated using geophysical data. Groundwater withdrawals were estimated using land-cover data, precipitation data, and published crop water-use data. For fields irrigated with surface water and groundwater, surface-water deliveries were subtracted from the estimated net irrigation requirement, and groundwater withdrawal was assumed to be equal to any demand unmet by surface water. The groundwater-flow model was calibrated to measured groundwater levels and stream base flows estimated using the base-flow index method. The model was calibrated through automated adjustments using statistical techniques through parameter estimation using the parameter estimation suite of software (PEST). PEST was used to adjust 273 parameters, grouped as hydraulic conductivity of the aquifer, spatial multipliers to recharge, temporal multipliers to recharge, and two specific recharge parameters. Base flow of the North Platte River at Bridgeport, Nebraska, streamgage near the eastern, downstream end of the model was one of the primary calibration targets. Simulated base flow reasonably matched estimated base flow for this streamgage during 1950–2008, with an average difference of 15 percent. Overall, 1950–2008 simulated base flow followed the trend of the estimated base flow reasonably well, in cases with generally increasing or decreasing base flow from the start of the simulation to the end. Simulated base flow also matched estimated base flow reasonably well for most of the North Platte River tributaries with estimated base flow. Average simulated groundwater budgets during 1989–2008 were nearly three times larger for irrigation seasons than for non-irrigation seasons. The calibrated groundwater-flow model was used with the Groundwater-Management Process for the 2005 version of the U.S. Geological Survey modular three-dimensional groundwater model, MODFLOW–2005, to provide a tool for the NPNRD to better understand how water-management decisions could affect stream base flows of the North Platte River at Bridgeport, Nebr., streamgage in a future period from 2008 to 2019 under varying climatic conditions. The simulation-optimization model was constructed to analyze the maximum increase in simulated stream base flow that could be obtained with the minimum amount of reductions in groundwater withdrawals for irrigation. A second analysis extended the first to analyze the simulated base-flow benefit of groundwater withdrawals along with application of intentional recharge, that is, water from canals being released into rangeland areas with sandy soils. With optimized groundwater withdrawals and intentional recharge, the maximum simulated stream base flow was 15–23 cubic feet per second (ft 3 /s) greater than with no management at all, or 10–15 ft 3 /s larger than with managed groundwater withdrawals only. These results indicate not only the amount that simulated stream base flow can be increased by these management options, but also the locations where the management options provide the most or least benefit to the simulated stream base flow. For the analyses in this report, simulated base flow was best optimized by reductions in groundwater withdrawals north of the North Platte River and in the western half of the area. Intentional recharge sites selected by the optimization had a complex distribution but were more likely to be closer to the North Platte River or its tributaries. Future users of the simulation-optimization model will be able to modify the input files as to type, location, and timing of constraints, decision variables of groundwater withdrawals by zone, and other variables to explore other feasible management scenarios that may yield different increases in simulated future base flow of the North Platte River.

Nebraska↗
USGS2011

Multilocus phylogeography and population structure of common eiders breeding in North America and Scandinavia

Aim Glacial refugia during the Pleistocene had major impacts on the levels and spatial apportionment of genetic diversity of species in northern latitude ecosystems. We characterized patterns of population subdivision, and tested hypotheses associated with locations of potential Pleistocene refugia and the relative contribution of these refugia to the post-glacial colonization of North America and Scandinavia by common eiders ( Somateria mollissima ). Specifically, we evaluated localities hypothesized as ice-free areas or glacial refugia for other Arctic vertebrates, including Beringia, the High Arctic Canadian Archipelago, Newfoundland Bank, Spitsbergen Bank and north-west Norway. Location Alaska, Canada, Norway and Sweden. Methods Molecular data from 12 microsatellite loci, the mitochondrial DNA (mtDNA) control region, and two nuclear introns were collected and analysed for 15 populations of common eiders ( n = 716) breeding throughout North America and Scandinavia. Population genetic structure, historical population fluctuations and gene flow were inferred using F -statistics, analyses of molecular variance, and multilocus coalescent analyses. Results Significant inter-population variation in allelic and haplotypic frequencies were observed (nuclear DNA F ST = 0.004–0.290; mtDNA Φ ST = 0.051–0.927). Whereas spatial differentiation in nuclear genes was concordant with subspecific designations, geographic proximity was more predictive of inter-population variance in mitochondrial DNA haplotype frequency. Inferences of historical population demography were consistent with restriction of common eiders to four geographic areas during the Last Glacial Maximum: Belcher Islands, Newfoundland Bank, northern Alaska and Svalbard. Three of these areas coincide with previously identified glacial refugia: Newfoundland Bank, Beringia and Spitsbergen Bank. Gene-flow and clustering analyses indicated that the Beringian refugium contributed little to common eider post-glacial colonization of North America, whereas Canadian, Scandinavian and southern Alaskan post-glacial colonization is likely to have occurred in a stepwise fashion from the same glacial refugium. Main conclusions Concordance of proposed glacial refugia used by common eiders and other Arctic species indicates that Arctic and subarctic refugia were important reservoirs of genetic diversity during the Pleistocene. Furthermore, suture zones identified at MacKenzie River, western Alaska/Aleutians and Scandinavia coincide with those identified for other Arctic vertebrates, suggesting that these regions were strong geographic barriers limiting dispersal from Pleistocene refugia.

Journal of Biogeography↗
USGS2024

Characterizing vegetation and return periods in avalanche paths using lidar and aerial imagery

Snow avalanches are a hazard and ecological disturbance across mountain landscapes worldwide. Understanding how avalanche frequency affects forests and vegetation improves infrastructure planning, risk management, and avalanche forecasting. We implemented a novel approach using lidar, aerial imagery, and a random forest model to classify imagery-observed vegetation within avalanche paths in southern Glacier National Park, Montana, USA. We calculated spatially explicit avalanche return periods using a physically based spatial interpolation method and characterized the vegetation within those return period zones. The automated vegetation classification model differed slightly between avalanche paths, but the combination of lidar and spectral signature metrics provided the best accuracy (88–92 percent) for predicting vegetation classes within complex avalanche terrain rather than lidar or spectral signature metrics alone. The highest frequency avalanche return periods were broadly characterized by grassland and shrubland, but the influence of topography greatly influences the vegetation classes as well as the return periods. Furthermore, statistically significant differences in lidar-derived vegetation canopy height exist between categorical return periods. The ability to characterize vegetation within various avalanche return periods using remote sensing data provides land use planners and avalanche forecasters a tool for assessing the spatial extent of large-magnitude avalanches in individual avalanche paths.

Montana↗
USGS2011

Effects of sample size, number of markers, and allelic richness on the detection of spatial genetic pattern

The influence of study design on the ability to detect the effects of landscape pattern on gene flow is one of the most pressing methodological gaps in landscape genetic research. To investigate the effect of study design on landscape genetics inference, we used a spatially-explicit, individual-based program to simulate gene flow in a spatially continuous population inhabiting a landscape with gradual spatial changes in resistance to movement. We simulated a wide range of combinations of number of loci, number of alleles per locus and number of individuals sampled from the population. We assessed how these three aspects of study design influenced the statistical power to successfully identify the generating process among competing hypotheses of isolation-by-distance, isolation-by-barrier, and isolation-by-landscape resistance using a causal modelling approach with partial Mantel tests. We modelled the statistical power to identify the generating process as a response surface for equilibrium and non-equilibrium conditions after introduction of isolation-by-landscape resistance. All three variables (loci, alleles and sampled individuals) affect the power of causal modelling, but to different degrees. Stronger partial Mantel r correlations between landscape distances and genetic distances were found when more loci were used and when loci were more variable, which makes comparisons of effect size between studies difficult. Number of individuals did not affect the accuracy through mean equilibrium partial Mantel r , but larger samples decreased the uncertainty (increasing the precision) of equilibrium partial Mantel r estimates. We conclude that amplifying more (and more variable) loci is likely to increase the power of landscape genetic inferences more than increasing number of individuals.

Molecular Ecology Resources↗
USGS2013

A comparison of data-driven groundwater vulnerability assessment methods

Increasing availability of geo-environmental data has promoted the use of statistical methods to assess groundwater vulnerability. Nitrate is a widespread anthropogenic contaminant in groundwater and its occurrence can be used to identify aquifer settings vulnerable to contamination. In this study, multivariate Weights of Evidence (WofE) and Logistic Regression (LR) methods, where the response variable is binary, were used to evaluate the role and importance of a number of explanatory variables associated with nitrate sources and occurrence in groundwater in the Milan District (central part of the Po Plain, Italy). The results of these models have been used to map the spatial variation of groundwater vulnerability to nitrate in the region, and we compare the similarities and differences of their spatial patterns and associated explanatory variables. We modify the standard WofE method used in previous groundwater vulnerability studies to a form analogous to that used in LR; this provides a framework to compare the results of both models and reduces the effect of sampling bias on the results of the standard WofE model. In addition, a nonlinear Generalized Additive Model has been used to extend the LR analysis. Both approaches improved discrimination of the standard WofE and LR models, as measured by the c -statistic. Groundwater vulnerability probability outputs, based on rank-order classification of the respective model results, were similar in spatial patterns and identified similar strong explanatory variables associated with nitrate source (population density as a proxy for sewage systems and septic sources) and nitrate occurrence (groundwater depth).

Milan District↗
USGS2020

Detecting commonality in multidimensional fish movement histories using sequence analysis

Background Acoustic telemetry, for tracking fish movement histories, is multidimensional capturing both spatial and temporal domains. Oftentimes, analyses of such data are limited to a single domain, one domain nested within the other, or ad hoc approaches that simultaneously consider both domains. Sequence analysis, on the other hand, offers a repeatable statistical framework that uses a sequence alignment algorithm to calculate pairwise dissimilarities among individual movement histories and then hierarchical agglomerative clustering to identify groups of fish with similar movement histories. The objective of this paper is to explore how acoustic telemetry data can be fit to this statistical framework and used to identify commonalities in the movement histories of acoustic-tagged sea lamprey during upstream migration through the St. Clair-Detroit River System. Results Five significant clusters were identified among individual fish. Clusters represented differences in timing of movements (short vs long duration in the Detroit R. and Lake St. Clair); extent of upstream migration (ceased migration in Lake St. Clair, lower St. Clair R., or upper St. Clair R.), and occurrence of fallback (return to Lake St. Clair after ceasing migration in the St. Clair R.). Inferences about sea lamprey distribution and behavior from these results were similar to those reached in a previous analysis using ad-hoc analysis methods. Conclusions The repeatable statistical framework outlined here can be used to group sea lamprey movement histories based on shared sequence characteristics (i.e., chronological order of “states” occupied). Further, this framework is flexible and allows researchers to define a priori the movement aspect (e.g., order, timing, duration) that is important for identifying both common or previously undetected movement histories. As such, we do not view sequence analysis as a panacea but as a useful complement to other modelling approaches (i.e., exploratory tool for informing hypothesis development) or a stand-alone semi-quantitative method for generating a simplified, temporally and spatially structured view of complex acoustic telemetry data and hypothesis testing when observed patterns warrant further investigation.

Michigan↗
USGS2014

Seismological and geodetic constraints on the 2011 M w 5.3 Trinidad, Colorado earthquake and induced deformation in the Raton Basin

The Raton Basin of southern Colorado and northern New Mexico is an actively produced hydrocarbon basin that has experienced increased seismicity since 2001, including the August 2011 M w 5.3 Trinidad normal faulting event. Following the 2011 earthquake, regional seismic observations were used to relocate 21 events, including the 2011 main shock, two foreshocks, and 13 aftershocks. Additionally, interferometric synthetic aperture radar (InSAR) observations of both the 2011 event and preevent basin deformation place constraint on the spatial kinematics of the 2011 event and localized basin subsidence due to ground water or gas withdrawal. We find that the 2011 earthquake ruptured an 8–10 km long segment of a normal fault at depths of 1.5–6.0 km within the crystalline Precambrian basement underlying the Raton Basin sedimentary rocks. The earthquake also nucleated within the crystalline basement in the vicinity of an active wastewater disposal site. The ensuing aftershock sequence demonstrated statistical properties expected for intraplate earthquakes, though the length of the 2011 earthquake is unexpectedly long for an M w 5.3 event, suggesting that wastewater disposal may have triggered a low stress drop, otherwise natural earthquake. Additionally, preevent and postevent seismicity in the Raton Basin spatially correlates to regions of subsidence observed in InSAR time series analysis. While these observations cannot discern a causal link between hydrocarbon production and seismicity, they constrain spatial relationships between active basin deformation and geological and anthropogenic features. Furthermore, the InSAR observations highlight the utility of space-based geodetic observations for monitoring and assessing anthropogenically induced and triggered deformation.

Journal of Geophysical Research B: Solid Earth↗
USGS1996

Methods and techniques for rigorous calibration of a cellular automaton model of urban growth

Several lessons about the process of calibration were learned during development of a self-modifying cellular automaton model to predict urban growth. This model, part of a global change research project on human-induced land transformations, was used to predict the spatial extent of urban growth 100 years into the future. The context of the prediction was to evaluate urban environmental disturbances such as land use conversion, urban heat island intensification, and greenhouse gas generation. Using data for the San Francisco Bay area as a test case, methods were developed, including interactive and statistical versions of the model, animation and visualization tools, automated testing methods, and Monte Carlo simulations. This presentation will enumerate, analyze, and discuss the lessons learned during the extensive process of model calibration. Experience with the methods developed may have broader use in assisting the rigorous calibration for other CA models, and perhaps those coupled environmental models with an extensive spatial data component. These methods are now under test as the project moves to a new data set for the Washington, D.C.-Baltimore area.

Conference Paper↗
USGS2021

Realizations

In statistics, a realization is an observed value of a random variable (Gubner 2006 ). In mathematical geology, the most important realizations are those in the form of maps of spatially correlated regionalized variables. Spatial description of random variables within complex domains and making certain decisions about those require complete knowledge of the attribute of interest at each point in space. However, it is virtually impossible to sample from every location within the domain to gain a complete spatial understanding of the random variables with certainty at different scales. Therefore, limited sampling leaves us with incomplete information, which is the source of uncertainty. Understanding the uncertainty and quantifying it are essential to minimize the risks of decision making. Geostatistical simulation techniques aim to quantify spatial uncertainty of random variables by numerically reproducing the reality, which we have limited knowledge of, in a discretized...

Book chapter↗
USGS2017

Viability analysis for multiple populations

Many species of conservation interest exist solely or largely in isolated populations. Ideally, prioritization of management actions among such populations would be guided by quantitative estimates of extinction risk, but conventional methods of demographic population viability analysis (PVA) model each population separately and require temporally extensive datasets that are rarely available in practice. We introduce a general class of statistical PVA that can be applied to many populations at once, which we term multiple population viability analysis or MPVA. The approach combines models of abundance at multiple spatial locations with temporal models of population dynamics, effectively borrowing information from more data-rich populations to inform inferences for data-poor populations. Covariates are used to explain population variability in space and time. Using Bayesian analysis, we illustrate the method with a dataset of Lahontan cutthroat trout ( Oncorhynchus clarkii henshawi ) observations that previously had been analyzed with conventional PVA. We find that MPVA predictions are similar in bias and higher in precision than predictions from simple PVA models that treat each population individually; moreover, the use of covariates in MPVA allows for predictions in minimally-sampled and unsampled populations. The basic MPVA model can be extended in multiple ways, such as by linking to a sampling and observation model to provide a full accounting of uncertainty. We conclude that the approach has great potential to expand the use of PVA for species that exist in multiple, isolated populations.

Biological Conservation↗
USGS1997

Statistical analysis of nitrate in ground water, West Salt River Valley, Arizona

Accurate estimates of the nitrate concentrations in ground water in west Salt River Valley are needed to better manage ground water affected by nitrate. Statistical analyses were done to establish the best statistical method to produce these estimates. Three sets of ground-water data for different time periods --1975-77, 1980-85, and 1986-90--were used to analyze spatial and temporal variations in concentrations of nitrate in ground water. The use of inverse-distance squared weighting, radial-basis function, kriging, and cokriging were evaluated for estimating nitrate concentrations in ground water. From an analysis of the cross-validation results, cokriging maps resulted in the best estimates, and they were accepted as being the most reliable. Cross-validation results also indicated that nitrate cokriged best with magnesium for 1975-77 and 1986-90 and with calcium for 1980-85. Kriging results consistently were almost as reliable as any of the cokriging results. Because of the difficulties inherent in the cokriging process, kriging, although not optimal, was the fastest way to obtain reasonably good results. In 1980-85, cokriged nitrate concentrations exceeded 20 milligrams per liter in a 12-square-kilometer area in Phoenix and Glendale and exceeded 10 milligrams per liter in a 280-square-kilometer area that extended to the Salt River. In 1986-90, nitrate concentrations along the entire reach of the Salt River in west Salt River Valley were less than 10 milligrams per liter and were smaller probably as a result of recharge from the Salt and Gila Rivers in 1982. Farther north in Phoenix and Glendale, the area in which nitrate concentrations exceeded 10 milligrams per liter expanded to 490 square kilometers for 1986-90. In Buckeye Valley, nitrate concentrations exceeded 10 milligrams per liter in an area of 300 square milometers for 1980-85 from the Gila River in the early 1980's but possibly could be an artifact of the different data distributions associated with each data set. In the Phoenix area, cokriged nitrate concentrations for 1975-77 exceeded 10 milligrams per liter in a 290-square-kilometer area and exceeded 20 milligrams per liter in a 1.4-square- kilometer area.

Water-Resources Investigations Report↗
USGS1999

Pattern and process of land loss in the Mississippi Delta: A spatial and temporal analysis of wetland habitat change

An earlier investigation (Turner 1997) concluded that most of the coastal wetland loss in Louisiana was caused by the effects of canal dredging, that loss was near zero in the absence of canals, and that land loss had decreased to near zero by the late 1990s. This analysis was based on a 15-min quadrangle (approximately 68,000 ha) scale that is too large to isolate processes responsible for small-scale wetland loss and too small to capture those responsible for large-scale loss. Herein, we conduct a further evaluation of the relationship between direct loss due to canal dredging and all other loss from 1933–1990 using a spatial scale of 4,100 ha that accurately captures local land-loss processes. Data sets for Pontchartrain and Breton basins did not meet the criteria for parametric statistics. Regressions of other wetland loss on canal area (i.e., direct loss) for the Birdfoot, Terrebonne, and Calcasieu basins were not significant. Significant positive curvilinear relationships were isolated for the Barataria (R 2 =0.40) and Mermentau (R 2 =0.29) basins, indicating that the extent of canals is significantly related to wetland loss in these basins. A significant negative relationship (R 2 =0.361) was found for the Atchafalaya coastal basin which had statistically lower loss rates than the other basins as a whole. When the data were combined for all basins, 9.2% of the variation in other wetland loss was attributable to canals. All significant regressions intercepted the Y-axis at positive loss values indicating that some loss occurred in the absence of canals. We agree with Turner that canals are an important agent in causing wetland loss in coastal Louisiana, but strongly disagree that they are responsible for the vast majority of this loss. We conclude that wetland loss in the Mississippi delta is an ongoing complex process involving several interacting factors and that efforts to create and restore Louisiana’s coastal wetlands must emphasize riverine inputs of freshwater and sediments.

Louisiana↗
USGS2023

Statewide sampling to determine spatial distribution, prevalence, and occurrence of per- and polyfluoroalkyl substances (PFAS) in Illinois community water supplies, 2020–21

Per- and polyfluoroalkyl substances (PFAS) are a group of synthetic chemicals that have been manufactured and used globally since the 1940s. PFAS are used for their oil- and water-repellent properties, ability to reduce friction, and their flame-retardant nature. PFAS are widely used in a variety of products, including clothing, carpet, food packaging, and firefighting foam. The properties that make them useful in manufacturing, however, also make them persistent and mobile, causing potential exposures to the environment and humans. Known as “forever chemicals,” these compounds resist degradation and have been determined to bioaccumulate in humans and wildlife. The Illinois Environmental Protection Agency (IEPA) collected a total of 1,711 samples (includes quality-control samples) of finished water at 1,428 entry points from 1,017 Illinois community water supply (CWS) systems and analyzed the water samples for PFAS. The results following confirmation samples indicated a mean of 99 percent of all sample results were below the minimum reporting level (MRL) of 2 nanograms per liter (ng/L). Of the detections at or above the MRL, 7 of 18 PFAS were detected in 149 of 1,428 entry points (about 10 percent). Of the nearly 7.4 million residents directly served by the CWS systems sampled, more than 1.3 million residents (about 18 percent) are served by CWS systems that had at least one detection of PFAS above the MRL of 2 ng/L. The most frequently detected PFAS were perfluorobutanesulfonic acid (about 6.2 percent, 37 ng/L maximum concentration), perfluorooctanesulfonic acid (PFOS) (about 5.0 percent, 150 ng/L maximum concentration), and perfluorooctanoic acid (PFOA) (about 4.8 percent, 25 ng/L maximum concentration). Of the 1,428 entry point samples from the CWS systems, 149 samples had confirmed detections of PFAS, with 93 of those 149 (about 62 percent) samples having at least one PFAS with a concentration that exceeded the median detected concentration of 3.2 ng/L. The highest concentrations detected were 150 ng/L (PFOS) and 140 ng/L (perfluorohexanesulfonic acid) at one CWS location which has been shut down and a different source of water has been provided to the consumers. Although PFAS detections were more common in CWS systems using surface-water sources (about 35 percent, 30 of 85) and mixed sources (50 percent, 5 of 10) compared to those using groundwater sources (about 9 percent, 114 of 1,333), a greater range of PFAS concentrations were observed in groundwater CWS systems (2 to 150 ng/L) than in surface-water CWS systems (2 to 15 ng/L). Statistically significant differences were determined between some detected PFAS (PFOA, PFOS, and perfluorohexanoic acid) and the source of drinking water (groundwater, surface water, or mixed). This report summarizes the occurrence and spatial distribution of PFAS in CWS systems across Illinois. The results from this sampling effort could be used by Illinois public health officials to identify the potential risk of PFAS in drinking water to human health.

Illinois↗
USGS2024

Developing a range-wide sampling framework for endangered species: A case study with light-footed Ridgway’s rail

Monitoring provides the foundation for evaluating recovery of endangered species, yet many species lack monitoring programs designed to integrate a species’ unique attributes, specific monitoring objectives, and principles of statistical sampling theory. We developed a framework for monitoring and assessment of endangered light-footed Ridgway’s rails ( Rallus obsoletus levipes ) across their U.S. range, relative to multi-scale recovery goals. We created spatially explicit sample units and a sampling frame covering all potential habitat to facilitate range-wide probability sampling, and also built a model of the call-broadcast process commonly used to survey marsh birds that included heterogeneity in availability for detection and conditional detectability for each bird during each survey. We used the model to simulate 96 sampling strategies that included different levels of replication, multiple approaches for sample allocation amongst strata, and both simple random and weighted probability sampling (i.e., weights proportional to local rail abundance) of sample units within strata. Effective monitoring surveyed ≥20–30% of the sampling frame on ≥3 occasions, with weighted sample selection and more targeted sampling (50% of units) for strata that are key to species recovery. We also tested Bayesian N-mixture models for estimating abundance and show that multiple models provide reasonable estimates. This work lays the foundation for statistical sampling and multi-scale population estimation for an endangered bird, and for refinement of abundance estimation models. Moreover, this work provides a replicable process for building customized and statistically defensible sampling frameworks to assess recovery of endangered species that can used for other sensitive species.

California↗
USGS2013

Spatially explicit models for inference about density in unmarked or partially marked populations

Recently developed spatial capture–recapture (SCR) models represent a major advance over traditional capture–recapture (CR) models because they yield explicit estimates of animal density instead of population size within an unknown area. Furthermore, unlike nonspatial CR methods, SCR models account for heterogeneity in capture probability arising from the juxtaposition of animal activity centers and sample locations. Although the utility of SCR methods is gaining recognition, the requirement that all individuals can be uniquely identified excludes their use in many contexts. In this paper, we develop models for situations in which individual recognition is not possible, thereby allowing SCR concepts to be applied in studies of unmarked or partially marked populations. The data required for our model are spatially referenced counts made on one or more sample occasions at a collection of closely spaced sample units such that individuals can be encountered at multiple locations. Our approach includes a spatial point process for the animal activity centers and uses the spatial correlation in counts as information about the number and location of the activity centers. Camera-traps, hair snares, track plates, sound recordings, and even point counts can yield spatially correlated count data, and thus our model is widely applicable. A simulation study demonstrated that while the posterior mean exhibits frequentist bias on the order of 5–10% in small samples, the posterior mode is an accurate point estimator as long as adequate spatial correlation is present. Marking a subset of the population substantially increases posterior precision and is recommended whenever possible. We applied our model to avian point count data collected on an unmarked population of the northern parula (Parula americana) and obtained a density estimate (posterior mode) of 0.38 (95% CI: 0.19–1.64) birds/ha. Our paper challenges sampling and analytical conventions in ecology by demonstrating that neither spatial independence nor individual recognition is needed to estimate population density—rather, spatial dependence can be informative about individual distribution and density.

Annals of Applied Statistics↗