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At least 613 records · Page 34Linked to original sources

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↗

USGS2002

Avian responses to late-season grazing in a shrub-willow floodplain

Riparian vegetation in western North America provides important habitat for breeding birds and valuable forage for grazing livestock. Whereas a number of studies have documented the response of riparian vegetation to the removal of cattle, few have experimentally evaluated specific grazing systems. We evaluated the responses of vegetation and breeding birds to two cycles of late-season (August–September) grazing followed by 34 months of rest on the Arapaho National Wildlife Refuge, Colorado. We used a before-and-after control-impact (BACI) design, with two control (ungrazed) and two treatment ( grazed) pastures composing the experimental units. Vegetation characteristics and bird densities were quantified on sample plots prior to and following two cycles of the treatment. We found no statistical differences in vegetation change and few differences in bird-density change among pastures. Inspection of means for pastures, however, suggests that changes in shrub vigor and spatial pattern differed among ungrazed and grazed pastures and that changes in population density for three of the nine bird species and three guilds studied differed among pastures. Our results suggest that habitat for grazing-sensitive birds may be restored while still allowing late-season grazing, although the rate at which species are recovered will be slower than if all cattle are removed.

Conservation Biology↗

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↗

USGS2026

Genetic structure in a previously extirpated population of gray wolves following reintroduction and natural recolonization

Genetic structuring in wildlife populations is driven by barriers that restrict gene flow as well as the history of population demography. Mechanisms driving genetic structuring can be nuanced in group-living species, such as gray wolves ( Canis lupus ). Behavioral factors, such as social affiliation and resistance, natal habitat imprinting, and trade-offs between dispersal from natal packs and territorial biding, affect habitat selection of wolves despite landscape barriers providing little resistance to their extensive dispersal capabilities. Wolves were previously extirpated from Idaho, USA, and current populations are the result of both reintroductions in 1995 and 1996 and natural dispersal from Canada. In this context we examined genetic structure of wolves in Idaho using 101 individuals genotyped at 18 nuclear DNA microsatellite loci and a subset of 38 individuals genotyped at 1019 single nucleotide polymorphism markers. We hypothesized panmictic (i.e., random mating) genetic structure in Idaho due to the long-distance dispersal abilities of gray wolves. Contrary to our hypothesis, we found three genetic clusters of gray wolves in Idaho, primarily supported by SNP markers. Microsatellite data suggested similar patterns, but permutation tests indicated these differences were not statistically significant. The extent of differentiation and evidence of gene flow, however, suggests that the three genetic clusters are not wholly isolated from one another. The distinctions between clusters spatially align with areas of reintroduction into central Idaho and Yellowstone National Park, as well ongoing natural recolonization from adjacent populations in Canada and Montana. Wolves at the periphery of analysis areas showed more admixture than those in the core, consistent with territoriality and mating behaviors contributing to genetic structuring. We demonstrate how management history, including reintroduction efforts, and animal behavior may interact and contribute to patterns of genetic structure in wild populations.

Idaho, Montana, Wyoming↗

USGS2019

Development and implementation of an empirical habitat change model and decision support tool for estuarine ecosystems

Widespread land use change in coastal ecosystems has led to a decline in the amount of habitat available for fish and wildlife, lower production of ecosystem goods and services, and loss of recreational and aesthetic value. This has prompted global efforts to restore the natural hydrologic regimes of developed shorelines, especially resource-rich estuaries, but the resilience of these restored ecosystems in the face of accelerated sea-level rise (SLR) remains uncertain. We implemented a Mo nitoring-based S imulation of A ccretion i n C oastal E s tuaries (MOSAICS) in R statistical software to address uncertainty in the resilience of modified estuarine habitats, using the Nisqually River Delta in the Pacific Northwest USA as a case study. MOSAICS is a spatially explicit model with a numerical foundation that uses empirical monitoring datasets to forecast habitat change in response to rising tidal levels. Because it accounts for the crucial ecomorphodynamic feedbacks between tidal inundation, vegetative growth, and sediment accretion, MOSAICS can be used to determine whether alternative management scenarios, such as enhanced sediment inputs, will bolster estuarine resilience to SLR. Under moderate SLR (0.62 m), the model predicted that a two-fold increase in mean daily suspended sediment during the rainy season was sufficient to maintain Nisqually’s emergent marshes through 2100, but under high SLR (1.35 m) MOSAICS indicated that greater sediment additions would be necessary to prevent submergence. A comparison between a restored marsh with subsided and high-elevation areas and a relict marsh demonstrated that the subsided restoration area was highly susceptible to SLR. Findings from the MOSAICS model highlight the importance of a site’s initial elevation, capacity for producing above and belowground biomass, and suspended sediment availability when considering management actions in estuaries and other coastal ecosystems.

Washington↗

USGS2016

Assessing the relationship between groundwater nitrate and animal feeding operations in Iowa (USA)

Nitrate-nitrogen is a common contaminant of drinking water in many agricultural areas of the United States of America (USA). Ingested nitrate from contaminated drinking water has been linked to an increased risk of several cancers, specific birth defects, and other diseases. In this research, we assessed the relationship between animal feeding operations (AFOs) and groundwater nitrate in private wells in Iowa. We characterized AFOs by swine and total animal units and type (open, confined, or mixed), and we evaluated the number and spatial intensities of AFOs in proximity to private wells. The types of AFO indicate the extent to which a facility is enclosed by a roof. Using linear regression models, we found significant positive associations between the total number of AFOs within 2 km of a well (p trend < 0.001), number of open AFOs within 5 km of a well (p trend < 0.001), and number of mixed AFOs within 30 km of a well (p trend < 0.001) and the log nitrate concentration. Additionally, we found significant increases in log nitrate in the top quartiles for AFO spatial intensity, open AFO spatial intensity, and mixed AFO spatial intensity compared to the bottom quartile (0.171 log(mg/L), 0.319 log(mg/L), and 0.541 log(mg/L), respectively; all p < 0.001). We also explored the spatial distribution of nitrate-nitrogen in drinking wells and found significant spatial clustering of high-nitrate wells (> 5 mg/L) compared with low-nitrate (≤ 5 mg/L) wells ( p = 0.001). A generalized additive model for high-nitrate status identified statistically significant areas of risk for high levels of nitrate. Adjustment for some AFO predictor variables explained a portion of the elevated nitrate risk. These results support a relationship between animal feeding operations and groundwater nitrate concentrations and differences in nitrate loss from confined AFOs vs. open or mixed types.

Iowa↗

USGS2021

Movement of synthetic organic compounds in the food web after the introduction of invasive quagga mussels (Dreissena bugensis) in Lake Mead, Nevada and Arizona, USA

Introductions of dreissenid mussels in North America have been a significant concern over the last few decades. This study assessed the distribution of synthetic organic compounds (SOCs) in the food web of Lake Mead, Nevada/Arizona, USA and how this distribution was influenced by the introduction of invasive quagga mussels. A clear spatial gradient of SOC concentrations in water was observed between lake basins downstream of populated areas and more rural areas. Within the food web, trophic magnification factors (TMF) indicated statistically significant biomagnification for nine, and biodilution for two, of 22 SOCs examined. The highest value recorded was for PCB 118 (TMF, 5.14), and biomagnification of methyl triclosan (TMF, 3.85) was also apparent. Biodilution was observed for Tonalide® (0.06) and Galaxolide® (0.38). Total SOC concentration in quagga mussels was higher than in three pelagic fishes. Also, 19 of 20 SOC examined in Largemouth Bass ( Micropterus salmoides ) had substantially lower concentrations in 2013, when quagga mussels had become well established, than in 2007/08, soon after quagga mussels were introduced. Estimates of SOC concentrations in the water column and quagga mussels suggest that a considerable portion (~10.5%) of the SOC mass in the lake has shifted from the pelagic to the benthic environments due to quagga mussel growth. These observations suggest that benthic species, such as the endangered Razorback Sucker, may be experiencing increased risk of SOC exposure. In addition, stable isotope analysis (carbon and nitrogen) indicated a decrease in the nutritional value of zooplankton to consumers (e.g., Razorback Sucker larvae) since quagga mussels became established. These changes could affect Razorback Sucker larval survival and recruitment. Results from this study strongly suggest that the introduction of quagga mussels has greatly altered the dynamics of SOCs and other processes in the food web of Lake Mead.

Arizona, Nevada↗

USGS2011

Shallow lithological structure across the Dead Sea Transform derived from geophysical experiments

In the framework of the DEad SEa Rift Transect (DESERT) project a 150 km magnetotelluric profile consisting of 154 sites was carried out across the Dead Sea Transform. The resistivity model presented shows conductive structures in the western section of the study area terminating abruptly at the Arava Fault. For a more detailed analysis we performed a joint interpretation of the resistivity model with a P wave velocity model from a partially coincident seismic experiment. The technique used is a statistical correlation of resistivity and velocity values in parameter space. Regions of high probability of a coexisting pair of values for the two parameters are mapped back into the spatial domain, illustrating the geographical location of lithological classes. In this study, four regions of enhanced probability have been identified, and are remapped as four lithological classes. This technique confirms the Arava Fault marks the boundary of a highly conductive lithological class down to a depth of ∼3 km. That the fault acts as an impermeable barrier to fluid flow is unusual for large fault zone, which often exhibit a fault zone characterized by high conductivity and low seismic velocity. At greater depths it is possible to resolve the Precambrian basement into two classes characterized by vastly different resistivity values but similar seismic velocities. The boundary between these classes is approximately coincident with the Al Quweira Fault, with higher resistivities observed east of the fault. This is interpreted as evidence for the original deformation along the DST originally taking place at the Al Quweira Fault, before being shifted to the Arava Fault.

Dead Sea↗

USGS2009

Climatic extremes improve predictions of spatial patterns of tree species

Understanding niche evolution, dynamics, and the response of species to climate change requires knowledge of the determinants of the environmental niche and species range limits. Mean values of climatic variables are often used in such analyses. In contrast, the increasing frequency of climate extremes suggests the importance of understanding their additional influence on range limits. Here, we assess how measures representing climate extremes (i.e., interannual variability in climate parameters) explain and predict spatial patterns of 11 tree species in Switzerland. We find clear, although comparably small, improvement (+20% in adjusted D 2 , +8% and +3% in cross-validated True Skill Statistic and area under the receiver operating characteristics curve values) in models that use measures of extremes in addition to means. The primary effect of including information on climate extremes is a correction of local overprediction and underprediction. Our results demonstrate that measures of climate extremes are important for understanding the climatic limits of tree species and assessing species niche characteristics. The inclusion of climate variability likely will improve models of species range limits under future conditions, where changes in mean climate and increased variability are expected.

Conference Paper↗

USGS2014

Evaluation of statistically downscaled GCM output as input for hydrological and stream temperature simulation in the Apalachicola–Chattahoochee–Flint River Basin (1961–99)

The accuracy of statistically downscaled general circulation model (GCM) simulations of daily surface climate for historical conditions (1961–99) and the implications when they are used to drive hydrologic and stream temperature models were assessed for the Apalachicola–Chattahoochee–Flint River basin (ACFB). The ACFB is a 50 000 km 2 basin located in the southeastern United States. Three GCMs were statistically downscaled, using an asynchronous regional regression model (ARRM), to ⅛° grids of daily precipitation and minimum and maximum air temperature. These ARRM-based climate datasets were used as input to the Precipitation-Runoff Modeling System (PRMS), a deterministic, distributed-parameter, physical-process watershed model used to simulate and evaluate the effects of various combinations of climate and land use on watershed response. The ACFB was divided into 258 hydrologic response units (HRUs) in which the components of flow (groundwater, subsurface, and surface) are computed in response to climate, land surface, and subsurface characteristics of the basin. Daily simulations of flow components from PRMS were used with the climate to simulate in-stream water temperatures using the Stream Network Temperature (SNTemp) model, a mechanistic, one-dimensional heat transport model for branched stream networks. The climate, hydrology, and stream temperature for historical conditions were evaluated by comparing model outputs produced from historical climate forcings developed from gridded station data (GSD) versus those produced from the three statistically downscaled GCMs using the ARRM methodology. The PRMS and SNTemp models were forced with the GSD and the outputs produced were treated as “truth.” This allowed for a spatial comparison by HRU of the GSD-based output with ARRM-based output. Distributional similarities between GSD- and ARRM-based model outputs were compared using the two-sample Kolmogorov–Smirnov (KS) test in combination with descriptive metrics such as the mean and variance and an evaluation of rare and sustained events. In general, precipitation and streamflow quantities were negatively biased in the downscaled GCM outputs, and results indicate that the downscaled GCM simulations consistently underestimate the largest precipitation events relative to the GSD. The KS test results indicate that ARRM-based air temperatures are similar to GSD at the daily time step for the majority of the ACFB, with perhaps subweekly averaging for stream temperature. Depending on GCM and spatial location, ARRM-based precipitation and streamflow requires averaging of up to 30 days to become similar to the GSD-based output. Evaluation of the model skill for historical conditions suggests some guidelines for use of future projections; while it seems correct to place greater confidence in evaluation metrics which perform well historically, this does not necessarily mean those metrics will accurately reflect model outputs for future climatic conditions. Results from this study indicate no “best” overall model, but the breadth of analysis can be used to give the product users an indication of the applicability of the results to address their particular problem. Since results for historical conditions indicate that model outputs can have significant biases associated with them, the range in future projections examined in terms of change relative to historical conditions for each individual GCM may be more appropriate.

Florida, Georgia↗