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Results for “Spatial Statistics”

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At least 919 records · Page 51Linked to original sources

USGS2004

A theory for modeling ground-water flow in heterogeneous media

Construction of a ground-water model for a field area is not a straightforward process. Data are virtually never complete or detailed enough to allow substitution into the model equations and direct computation of the results of interest. Formal model calibration through optimization, statistical, and geostatistical methods is being applied to an increasing extent to deal with this problem and provide for quantitative evaluation and uncertainty analysis of the model. However, these approaches are hampered by two pervasive problems: 1) nonlinearity of the solution of the model equations with respect to some of the model (or hydrogeologic) input variables (termed in this report system characteristics) and 2) detailed and generally unknown spatial variability (heterogeneity) of some of the system characteristics such as log hydraulic conductivity, specific storage, recharge and discharge, and boundary conditions. A theory is developed in this report to address these problems. The theory allows construction and analysis of a ground-water model of flow (and, by extension, transport) in heterogeneous media using a small number of lumped or smoothed system characteristics (termed parameters). The theory fully addresses both nonlinearity and heterogeneity in such a way that the parameters are not assumed to be effective values. The ground-water flow system is assumed to be adequately characterized by a set of spatially and temporally distributed discrete values, ?, of the system characteristics. This set contains both small-scale variability that cannot be described in a model and large-scale variability that can. The spatial and temporal variability in ? are accounted for by imagining ? to be generated by a stochastic process wherein ? is normally distributed, although normality is not essential. Because ? has too large a dimension to be estimated using the data normally available, for modeling purposes ? is replaced by a smoothed or lumped approximation y?. (where y is a spatial and temporal interpolation matrix). Set y?. has the same form as the expected value of ?, y 'line' ? , where 'line' ? is the set of drift parameters of the stochastic process; ?. is a best-fit vector to ?. A model function f(?), such as a computed hydraulic head or flux, is assumed to accurately represent an actual field quantity, but the same function written using y?., f(y?.), contains error from lumping or smoothing of ? using y?.. Thus, the replacement of ? by y?. yields nonzero mean model errors of the form E(f(?)-f(y?.)) throughout the model and covariances between model errors at points throughout the model. These nonzero means and covariances are evaluated through third and fifth-order accuracy, respectively, using Taylor series expansions. They can have a significant effect on construction and interpretation of a model that is calibrated by estimating ?.. Vector ?.. is estimated as 'hat' ? using weighted nonlinear least squares techniques to fit a set of model functions f(y'hat' ?) to a. corresponding set of observations of f(?), Y. These observations are assumed to be corrupted by zero-mean, normally distributed observation errors, although, as for ?, normality is not essential. An analytical approximation of the nonlinear least squares solution is obtained using Taylor series expansions and perturbation techniques that assume model and observation errors to be small. This solution is used to evaluate biases and other results to second-order accuracy in the errors. The correct weight matrix to use in the analysis is shown to be the inverse of the second-moment matrix E(Y-f(y?.))(Y-f(y?.))', but the weight matrix is assumed to be arbitrary in most developments. The best diagonal approximation is the inverse of the matrix of diagonal elements of E(Y-f(y?.))(Y-f(y?.))', and a method of estimating this diagonal matrix when it is unknown is developed using a special objective function to compute 'hat' ?. When considered to be an estimate of f

Professional Paper↗

USGS1996

Ground-water hydrology, historical water use, and simulated ground-water flow in Cretaceous-age Coastal Plain aquifers near Charleston and Florence, South Carolina

A quasi-three-dimensional, transient, digital, ground-water flow model representing the Coastal Plain aquifers of South Carolina, has been constructed to assist in defining the ground- water-flow system of Cretaceous aquifers near Charleston and Florence, S.C. Both cities are near the centers of large (greater than 150 feet) potentiometric declines in the Middendorf aquifer. In 1989, the diameter of the depressions was approximately 40 miles at Charleston and 15 miles at Florence. The potentiometric decline occurred between predevelopment (1926) and 1982 near Florence, and between predevelopment (1879) and 1989 near Charleston. The city of Charleston does not withdraw water from these aquifers; however, some of the small communities in the area use these aquifers for a potable water supply. The model simulates flow in and between four aquifer systems. The model has a variable-cell-size grid, and spans the Coastal Plain from the Savannah River in the southwest to the Cape Fear Arch in the northeast, and from the Fall Line in the northwest to approximately 30 miles offshore to the southeast. Model-grid cell size is 1 by 1 mile in a 48 by 48 mile area centered in Charleston, and in a 36 by 48 mile area centered in Florence. The model cell size gradually increases to a maximum of 4 by 4 miles outside the two study areas. The entire grid consists of 115 by 127 cells and covers an area of 39,936 square miles. The model was calibrated to historical water-level data. The calibration relied on three techniques: (1) matching simulated and observed potentiometric map surfaces, (2) statistical comparison of observed and simulated heads, and (3) comparison of observed and simulated well hydrographs. Systematic changes in model parameters showed that simulated heads are most sensitive to changes in aquifer transmissivity. Eight predictive ground-water-use scenarios were simulated for the Mount Pleasant area, which presently (1993) uses the Middendorf aquifer as a sole-source of potable water. These simulations use various combinations of spatial distribution, and injection of treated wastewater effluent for existing and future Middendorf aquifer wells.

South Carolina↗

USGS2014

Using spatial resampling to assess redd count survey length requirements for Pacific Lamprey

Pacific Lamprey Entosphenus tridentatus has declined across its range along the West Coast of North America, and an understanding of all life history phases is needed to address population recovery. Spawning surveys (redd counts) are common tools currently used to monitor returning adult salmonids, but such methodologies are in their infancy for Pacific Lamprey. Our objective was to assess the minimum spawning survey distance required to detect the presence of Pacific Lamprey redds and obtain precise redd density estimates from these data. To do this, we statistically resampled existing spawning locations of Pacific Lamprey collected during spawning surveys in four streams of the Willamette River Basin, Oregon, during spring of 2013. We found that the minimum survey distance for Pacific Lamprey redd detection was inversely related to the observed redd density and was always less than 1.2 km. Survey distance requirements to obtain precise redd counts (±20% of observed redd densities) were also inversely related to redd density and habitat availability, and varied between 1.3 km and 13.7 km. Our results suggest that spawning surveys are a potential tool for monitoring adult Pacific Lamprey abundance, but the specific objectives of the monitoring programs and acknowledgment of unknowns must be considered prior to implementation into recovery plans.

Oregon↗

USGS2023

A genetic warning system for a hierarchically structured wildlife monitoring framework

Genetic variation is a well-known indicator of population fitness yet is not typically included in monitoring programs for sensitive species. Additionally, most programs monitor populations at one scale, which can lead to potential mismatches with ecological processes critical to species' conservation. Recently developed methods generating hierarchically nested population units (i.e., clusters of varying scales) for greater sage-grouse ( Centrocercus urophasianus ) have identified population trend declines across spatiotemporal scales to help managers target areas for conservation. The same clusters used as a proxy for spatial scale can alert managers to local units (i.e., neighborhood-scale) with low genetic diversity, further facilitating identification of management targets. We developed a genetic warning system utilizing previously developed hierarchical population units to identify management-relevant areas with low genetic diversity within the greater sage-grouse range. Within this warning system we characterized conservation concern thresholds based on values of genetic diversity and developed a statistical model for microsatellite data to robustly estimate these values for hierarchically nested populations. We found that 41 of 224 neighborhood-scale clusters had low genetic diversity, 23 of which were coupled with documented local population trend decline. We also found evidence of cross-scale low genetic diversity in the small and isolated Washington population, unlikely to be reversed through typical local management actions alone. The combination of low genetic diversity and a declining population suggests relatively high conservation concern. Our findings could further facilitate conservation action prioritization in combination with population trend assessments and (or) local information, and act as a base-line of genetic diversity for future comparison. Importantly, the approach we used is broadly applicable across taxa.

Ecological Applications↗

USGS2000

Combining accuracy assessment of land-cover maps with environmental monitoring programs

A scientifically valid accuracy assessment of a large-area, land-cover map is expensive. Environmental monitoring programs offer a potential source of data to partially defray the cost of accuracy assessment while still maintaining the statistical validity. In this article, three general strategies for combining accuracy assessment and environmental monitoring protocols are described. These strategies range from a fully integrated accuracy assessment and environmental monitoring protocol, to one in which the protocols operate nearly independently. For all three strategies, features critical to using monitoring data for accuracy assessment include compatibility of the land-cover classification schemes, precisely co-registered sample data, and spatial and temporal compatibility of the map and reference data. Two monitoring programs, the National Resources Inventory (NRI) and the Forest Inventory and Monitoring (FIM), are used to illustrate important features for implementing a combined protocol.

Environmental Monitoring and Assessment↗

USGS2025

Effect of passive integrated transponder tag size on survival, tag loss, and growth of Santa Ana Sucker

Objective The Santa Ana Sucker Pantosteus santaanae is endemic to southern California and is listed as threatened under the U.S. Endangered Species Act. Seasonal limitations on conventional sampling and inconsistencies in survey methodologies have led to an incomplete understanding of population dynamics. Alternative sampling methods have the potential to fill important knowledge gaps in biology and life history. One option is to use passive integrated transponder (PIT) tags to identify individuals and track their movements. The objective of this study was to test the effect of PIT tag size on survival, tag loss, and growth of Santa Ana Suckers. Methods Sixty-one Santa Ana Suckers were randomly assigned to one of three treatments (control: N = 20; 8-mm PIT tag: N = 21; or 12-mm PIT tag: N = 20). A full-duplex PIT tag was injected into each fish, and lengths (standard, fork, and total length) and weight were recorded. Fish remained in raceways for 36 d, after which they were retrieved, scanned, and remeasured. Results There were five fish mortalities within 48 h of tagging, resulting in an 87.8% overall survival rate. There were two fish mortalities from the 8-mm tag treatment and three mortalities from the 12-mm tag treatment. One fish from each tagging treatment lost their tag, resulting in a 5.6% overall tag loss rate. The Fisher’s exact test indicated no statistically significant difference in survival or tag loss between treatments. The ordinary least squares regression detected no effect of tag size on growth for Santa Ana Suckers. Conclusions Results suggest that 8- and 12-mm PIT tags are suitable options for tagging Santa Ana Suckers measuring at least 62 and 70 mm fork length, respectively. Supplementing the current sampling strategy with PIT tags could help monitoring efforts to expand spatially and temporally while providing expanded population data to inform adaptive management actions for the Santa Ana Sucker.

North American Journal of Fisheries Management↗

USGS2009

The Ecology of Parasite-Host Interactions at Montezuma Well National Monument, Arizona - Appreciating the Importance of Parasites

Although parasites play important ecological roles through the direct interactions they have with their hosts, historically that fact has been underappreciated. Today, scientists have a growing appreciation of the scope of such impacts. Parasites have been reported to dominate food webs, alter predator-prey relationships, act as ecosystem engineers, and alter community structure. In spite of this growing awareness in the scientific community, parasites are still often neglected in the consideration of the management and conservation of resources and ecosystems. Given that at least half of the organisms on earth are probably parasitic, it should be evident that the ecological functions of parasites warrant greater attention. In this report, we explore different aspects of parasite-host relationships found at a desert spring pond within Montezuma Well National Monument, Arizona. In three separate but related chapters, we explore interactions between a novel amphipod host and two parasites. First, we identify how host behavior responds to this association and how this association affects interactions with both invertebrate non-host predators and a vertebrate host predator. Second, we look at the human dimension, investigating how human recreation can indirectly affect patterns of disease by altering patterns of vertebrate host space use. Finally - because parasites and diseases are of increasing importance in the management of wildlife species, especially those that are imperiled or of management concern - the third chapter argues that research would benefit from increased attention to the statistical analysis of wildlife disease studies. This report also explores issues of statistical parasitology, providing information that may better inform those designing research projects and analyzing data from studies of wildlife disease. In investigating the nature of parasite-host interactions, the role that relationships play in ecological communities, and how human activities alter these associations, scientists usually make inferences by methods of statistical hypotheses testing. This type of hypothesis testing places additional importance on the analysis and interpretation of parasite-host interactions. We address these ideas in this report, focusing on the following questions: (1) How do two parasites with complex life cycles alter the behavior of a novel amphipod host, and how do host and non-host predators respond to infected amphipod prey? (2) Does human recreation affect spatial patterns of infection in an otherwise natural ecosystem? (3) How is hypothesis-testing applied in studies of wildlife disease? (4) What conclusions can we make about the relative usefulness of these methodologies? and (5) How can the analysis and interpretation of wildlife disease studies be improved? Each chapter of this report contains its own literature-cited section, with tables included in appendixes at the end of the full report.

Open-File Report↗

USGS2025

Characterization of suspended sediment flux and streamflow trends in the Fountain Creek watershed, Colorado, 1998 through 2022

The U.S. Geological Survey evaluated long-term suspended sediment flux and streamflow datasets for temporal trends (monotonic and step trends) at 10 streamgage sites within the Fountain Creek watershed in central Colorado using the Mann-Kendall test (monotonic trend) and the Wilcoxon signed-rank test (step trend). Data were collected in cooperation with Colorado Springs Stormwater Enterprise. In this study, 10 sites with long-term suspended sediment records were evaluated during their operational periods, which ranged from 1998 through 2022. To assess how stream behavior might relate to shifts in suspended sediment transport, the Richards-Baker flashiness index, a measure of flashiness, was evaluated for each site. The Richards-Baker flashiness index was calculated for the same months as streamflow and suspended sediment (April through September) using all available streamflow data for a given site. Additionally, cumulative double-mass curves were developed to define temporal variation in the relation between streamflow and suspended sediment loads. This was completed by assessing differences in slopes before and after an observed break in the double-mass curve plots. Five streamgage sites showed statistically significant ( p <0.05) negative trends for suspended sediment flux, and nonsignificant decreases were indicated at the other five sites. The statistically significant negative trends are distributed across the watershed and include smaller tributaries and the main stem of Fountain Creek closer to its confluence with the Arkansas River. Such a broad distribution of negative trends is most likely an indication of improved water quality in the watershed with regards to suspended sediment. Assessing causes for the decreases in suspended sediment loads is beyond the scope of this report; however, spatially distributed bank erosion control projects, stormflow retention projects in the watershed, and changes in climate and storm patterns could be some of the potential drivers of the suspended sediment load trends in this watershed. The flashiness of the streamflow was assessed and can be dismissed on the basis of this study as a potential driver of trends in suspended sediment flux because the magnitude of changes was found to be negligible at all sites.

Colorado↗

USGS2002

Contrasting determinants of abundance in ancestral and colonized ranges of an invasive brood parasite

Avian species distributions are typically regarded as constrained by spatially extensive variables such as climate, habitat, spatial patchiness, and microhabitat attributes. We hypothesized that the distribution of a brood parasite depends as strongly on host distribution patterns as on biophysical factors and examined this hypothesis with respect to the national distribution of the Brown-headed Cowbird (Molothrus ater). We applied a classification and regression (CART) analysis to data from the Breeding Bird Survey (BBS) and the Christmas Bird Count (CBC) and derived hierarchically organized statistical models of the influence of climate and weather, cropping and land use, and host abundance and distribution on the distribution of the Brown-headed Cowbird within the conterminous United States. The model accounted for 47.2% of the variation in cowbird incidence, and host abundance was the top predictor with an R2 of 18.9%. The other predictors identified by the model (crops 15.7%, weather and climate 14.3%, and region 9.6%) fit the ecological profile of this cowbird. We showed that host abundance was independent of these environmental predictors of cowbird distribution. At the regional scale host abundance played a very strong role in determining cowbird abundance in the cowbird?s colonized range east and west of their ancestral range in the Great Plains (26.6%). Crops were not a major predictor for cowbirds in their ancestral range, although they are the most important predictive factor (33%) for the grassland passerines that are the cowbird?s ancestral hosts. Consequently our findings suggest that the distribution of hosts does indeed take precedence over habitat attributes in shaping the cowbird?s distribution at a national scale, within an envelope of constraint set by biophysical factors.

Book chapter↗

USGS2014

Estimating landscape resistance to dispersal

Dispersal is an inherently spatial process that can be affected by habitat conditions in sites encountered by dispersers. Understanding landscape resistance to dispersal is important in connectivity studies and reserve design, but most existing methods use resistance functions with cost parameters that are subjectively chosen by the investigator. We develop an analytic approach allowing for direct estimation of resistance parameters that folds least cost path methods typically used in simulation approaches into a formal statistical model of dispersal distributions. The core of our model is a frequency distribution of dispersal distances expressed as least cost distance rather than Euclidean distance, and which includes terms for feature-specific costs to dispersal and sex (or other traits) of the disperser. The model requires only origin and settlement locations for multiple individuals, such as might be obtained from mark–recapture studies or parentage analyses, and maps of the relevant habitat features. To evaluate whether the model can estimate parameters correctly, we fit our model to data from simulated dispersers in three kinds of landscapes (in which resistance of environmental variables was categorical, continuous with a patchy configuration, or continuous in a trend pattern). We found maximum likelihood estimators of resistance and individual trait parameters to be approximately unbiased with moderate sample sizes. We applied the model to a small grizzly bear dataset to demonstrate how this approach could be used when the primary interest is in the prediction of costs and found that estimates were consistent with expectations based on bear ecology. Our method has important practical applications for testing hypotheses about dispersal ecology and can be used to inform connectivity planning efforts, via the resistance estimates and confidence intervals, which can be used to create a data-driven resistance surface.

Landscape Ecology↗

USGS2014

Volcanology: Lessons learned from Synthetic Aperture Radar imagery

Twenty years of continuous Earth observation by satellite SAR have resulted in numerous new insights into active volcanism, including a better understanding of subsurface magma storage and transport, deposition of volcanic materials on the surface, and the structure and development of volcanic edifices. This massive archive of data has resulted in fundamental leaps in our understanding of how volcanoes work – for example, identifying magma accumulation at supposedly quiescent volcanoes, even in remote areas or in the absence of ground-based data. In addition, global compilations of volcanic activity facilitate comparison of deformation behavior between different volcanic arcs and statistical evaluation of the strong link between deformation and eruption. SAR data are also increasingly used in timely hazard evaluation thanks to decreases in data latency and growth in processing and analysis techniques. The existing archive of SAR imagery is on the cusp of being enhanced by a new generation of satellite SAR missions, in addition to ground-based and airborne SAR systems, which will provide enhanced temporal and spatial resolution, broader geographic coverage, and improved availability of data to the scientific community. Now is therefore an opportune time to review the contributions of SAR imagery to volcano science, monitoring, and hazard mitigation, and to explore the future potential for SAR in volcanology. Provided that the ever-growing volume of SAR data can be managed effectively, we expect the future application of SAR data to expand from being a research tool for analyzing volcanic activity after the fact, to being a monitoring and research tool capable of imaging a wide variety of processes on different temporal and spatial scales as those processes are occurring. These data can then be used to develop new models of how volcanoes work and to improve quantitative forecasts of volcanic activity as a means of mitigating risk from future eruptions.

Journal of Volcanology and Geothermal Research↗

USGS2015

Optimal population prediction of sandhill crane recruitment based on climate-mediated habitat limitations

Prediction is fundamental to scientific enquiry and application; however, ecologists tend to favour explanatory modelling. We discuss a predictive modelling framework to evaluate ecological hypotheses and to explore novel/unobserved environmental scenarios to assist conservation and management decision-makers. We apply this framework to develop an optimal predictive model for juvenile (<1 year old) sandhill crane Grus canadensis recruitment of the Rocky Mountain Population (RMP). We consider spatial climate predictors motivated by hypotheses of how drought across multiple time-scales and spring/summer weather affects recruitment. Our predictive modelling framework focuses on developing a single model that includes all relevant predictor variables, regardless of collinearity. This model is then optimized for prediction by controlling model complexity using a data-driven approach that marginalizes or removes irrelevant predictors from the model. Specifically, we highlight two approaches of statistical regularization, Bayesian least absolute shrinkage and selection operator (LASSO) and ridge regression. Our optimal predictive Bayesian LASSO and ridge regression models were similar and on average 37% superior in predictive accuracy to an explanatory modelling approach. Our predictive models confirmed a priori hypotheses that drought and cold summers negatively affect juvenile recruitment in the RMP. The effects of long-term drought can be alleviated by short-term wet spring–summer months; however, the alleviation of long-term drought has a much greater positive effect on juvenile recruitment. The number of freezing days and snowpack during the summer months can also negatively affect recruitment, while spring snowpack has a positive effect. Breeding habitat, mediated through climate, is a limiting factor on population growth of sandhill cranes in the RMP, which could become more limiting with a changing climate (i.e. increased drought). These effects are likely not unique to cranes. The alteration of hydrological patterns and water levels by drought may impact many migratory, wetland nesting birds in the Rocky Mountains and beyond. Generalizable predictive models (trained by out-of-sample fit and based on ecological hypotheses) are needed by conservation and management decision-makers. Statistical regularization improves predictions and provides a general framework for fitting models with a large number of predictors, even those with collinearity, to simultaneously identify an optimal predictive model while conducting rigorous Bayesian model selection. Our framework is important for understanding population dynamics under a changing climate and has direct applications for making harvest and habitat management decisions.

Journal of Animal Ecology↗

USGS2013

The environmental-data automated track annotation (Env-DATA) system: linking animal tracks with environmental data

The movement of animals is strongly influenced by external factors in their surrounding environment such as weather, habitat types, and human land use. With advances in positioning and sensor technologies, it is now possible to capture animal locations at high spatial and temporal granularities. Likewise, scientists have an increasing access to large volumes of environmental data. Environmental data are heterogeneous in source and format, and are usually obtained at different spatiotemporal scales than movement data. Indeed, there remain scientific and technical challenges in developing linkages between the growing collections of animal movement data and the large repositories of heterogeneous remote sensing observations, as well as in the developments of new statistical and computational methods for the analysis of movement in its environmental context. These challenges include retrieval, indexing, efficient storage, data integration, and analytical techniques.

Movement Ecology↗

USGS2014

Radar analysis of fall bird migration stopover sites in the northeastern U.S.

The national network of weather surveillance radars (WSR-88D) detects flying birds and is a useful remote-sensing tool for ornithological study. We used data collected during fall 2008 and 2009 by 16 WSR-88D radars in the northeastern U.S. to quantify the spatial distribution of landbirds during migratory stopover. We geo-referenced estimates based on radar reflectivity, of the density of migrants aloft at their abrupt evening exodus from daytime stopover sites, to the approximate locations from which they emerged. We classified bird stopover use by the magnitude and variation of radar reflectivity across nights; areas were considered “important” stopover sites for conservation if bird density was consistently high. We developed statistical models that predict potentially important stopover sites across the region, based on land cover, ground elevation, and geographic location. Large areas of regionally important stopover sites were located along the coastlines of Long Island Sound, throughout the Delmarva Peninsula, in areas surrounding Baltimore and Washington, along the western edge of the Adirondack Mountains, and within the Appalachian Mountains of southwestern Virginia and West Virginia. Locally important stopover sites generally were associated with deciduous forests embedded within landscapes dominated by developed or agricultural lands, or near the shores of major water bodies. Preserving or enhancing patches of natural habitat, particularly deciduous forests, in developed or agricultural landscapes and along major coastlines could be a priority for conservation plans addressing the stopover requirements of migratory landbirds in the northeastern U.S. Our maps of important stopover sites can be used to focus conservation efforts and can serve as a sampling frame for fieldwork to validate radar observations or for ecological studies of landbirds on migratory stopover.

The Condor↗

USGS2012

Stress rotation during the Coalinga aftershock sequence

This study considers spatial and temporal changes of the stress regime during the 1983 Coalinga aftershock sequence. In both cases the observed changes manifest themselves as rotations of the most compressive stress axis. Inversion of 165 M ≥3 aftershocks shows that in the southern aftershock zone the azimuth of the most compressional stress axis is oriented 24°E of north, while in the northern and central zones it is oriented 51°E of north. Statistical analysis by use of non-parametric bootstrap resampling shows that this rotation is significant at above the 95% confidence level. Inverting the focal mechanisms of 122 M ≥3 aftershocks in the central and northern aftershock zones reveals a rotation of the principal stress axes during the first 500 days after the main shock. The compressional axis rotates from an azimuth of 62°E of north to 47°E of north. Statistical analysis by use of nonparametric bootstrap resampling shows that this rotation is just significantly different from zero at the 95% confidence level, subject to the assumption that 80% of the fault planes can be correctly picked from the two nodal planes. As the number of fault planes picked correctly varies from 50% (random choices) to 100% the significance of the rotation varies from 87% to 98%. Further investigation fails to find the signature of the stress changes predicted by dislocation theory in the focal mechanisms of the aftershocks. Active folds in the area suggest that the pre-main shock stress regime had a compressional axis with a NE-SW orientation. Thus the rotation during the aftershock sequence is thought to be a rebound from a stress anomaly induced by the main shock. This stress anomaly may be causally related to the occurrence of the aftershocks.

Journal of Geophysical Research Solid Earth↗

USGS2011

Projected changes to growth and mortality of Hawaiian corals over the next 100 years

Background: Recent reviews suggest that the warming and acidification of ocean surface waters predicated by most accepted climate projections will lead to mass mortality and declining calcification rates of reef-building corals. This study investigates the use of modeling techniques to quantitatively examine rates of coral cover change due to these effects. Methodology/Principal Findings: Broad-scale probabilities of change in shallow-water scleractinian coral cover in the Hawaiian Archipelago for years 2000-2099 A.D. were calculated assuming a single middle-of-the-road greenhouse gas emissions scenario. These projections were based on ensemble calculations of a growth and mortality model that used sea surface temperature (SST), atmospheric carbon dioxide (CO2), observed coral growth (calcification) rates, and observed mortality linked to mass coral bleaching episodes as inputs. SST and CO2 predictions were derived from the World Climate Research Programme (WCRP) multi-model dataset, statistically downscaled with historical data. Conclusions/Significance: The model calculations illustrate a practical approach to systematic evaluation of climate change effects on corals, and also show the effect of uncertainties in current climate predictions and in coral adaptation capabilities on estimated changes in coral cover. Despite these large uncertainties, this analysis quantitatively illustrates that a large decline in coral cover is highly likely in the 21st Century, but that there are significant spatial and temporal variances in outcomes, even under a single climate change scenario.

PLoS ONE↗

USGS2015

Desert tortoises ( Gopherus agassizii ) are selective herbivores that track the flowering phenology of their preferred food plants

Previous studies of desert tortoise foraging ecology in the western Mojave Desert suggest that these animals are selective herbivores, which alter their diet according to the temporal availability of preferred food plants. These studies, however, did not estimate availability of potential food plants by taking into account the spatial and temporal variability in ephemeral plant abundance that occurs within the spring season. In this study, we observed 18 free-ranging adult tortoises take 35,388 bites during the spring foraging season. We also estimated the relative abundance of potential food plants by stratifying our sampling across different phenological periods of the 3-month long spring season and by different habitats and microhabitats. This methodology allowed us to conduct statistical tests comparing tortoise diet against plant abundance. Our results show that tortoises choose food plants non-randomly throughout the foraging season, a finding that corroborates the hypothesis that desert tortoises rely on key plants during different phenological periods of spring. Moreover, tortoises only consumed plants in a succulent state until the last few weeks of spring, at which time most annuals and herbaceous perennials had dried and most tortoises had ceased foraging. Many species of food plants—including several frequently eaten species—were not detected in our plant surveys, yet tortoises located these rare plants in their home ranges. Over 50% of bites consumed were in the group of undetected species. Interestingly, tortoises focused heavily on several leguminous species, which could be nutritious foods owing to their presumably high nitrogen contents. We suggest that herbaceous perennials, which were rare on our study area but represented ~30% of tortoise diet, may be important in sustaining tortoise populations during droughts when native annuals are absent. These findings highlight the vulnerability of desert tortoises to climate change if such changes alter the availability of their preferred food plants.

California↗

USGS2020

Combining models of the critical streakline and the cross-sectional distribution of juvenile salmon to predict fish routing at river junctions

Because fish that enter the interior Delta have poorer survival than those emigrating via the Sacramento River, understanding the mechanisms that drive entrainment rates at side channel junctions is critically important for the management of imperiled juvenile salmon. Here, we implement a previously proposed process-based conceptual model to study entrainment rates based on three linked elements: the entrainment zone, critical streakline, and cross-sectional distribution of fish. The critical streakline is the location along a channel cross-section immediately upstream of a junction that forms the spatial divide between parcels of water that enter a side channel or remain in the main channel. The critical streakline therefore divides the main channel into entrainment zones within which fish would likely enter each channel. Combined with information about the cross-sectional distribution of fish upstream of a junction, this conceptual model provides a means to predict fish entrainment into each channel. To apply this conceptual model, we combined statistical models of the critical streakline, the cross-sectional distribution of acoustic tagged juvenile Chinook salmon, and their probability of entrainment into Georgiana Slough. We fit joint beta regression and logistic regression models to acoustic telemetry data gathered in 2011 and 2012 to estimate the cross-sectional distribution of fish upstream of the junction, and to estimate the probability of entrainment for fish on either side of the critical streakline. We show that entrainment rates can be predicted by understanding how the combination of critical streakline position and cross-sectional distribution of fish co-vary as a function of environmental covariates. By integrating over individual positions and entrainment fates to arrive at population-level entrain probability in relation to environmental covariates, our model offers managers a simple but powerful tool to evaluate how alternative actions affect migrating fish.

California↗