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At least 847 records · Page 47Linked to original sources

USGS2011

Improving national-scale invasion maps: Tamarisk in the western United States

New invasions, better field data, and novel spatial-modeling techniques often drive the need to revisit previous maps and models of invasive species. Such is the case with the at least 10 species of Tamarix , which are invading riparian systems in the western United States and expanding their range throughout North America. In 2006, we developed a National Tamarisk Map by using a compilation of presence and absence locations with remotely sensed data and statistical modeling techniques. Since the publication of that work, our database of Tamarix distributions has grown significantly. Using the updated database of species occurrence, new predictor variables, and the maximum entropy (Maxent) model, we have revised our potential Tamarix distribution map for the western United States. Distance-to-water was the strongest predictor in the model (58.1%), while mean temperature of the warmest quarter was the second best predictor (18.4%). Model validation, averaged from 25 model iterations, indicated that our analysis had strong predictive performance (AUC = 0.93) and that the extent of Tamarix distributions is much greater than previously thought. The southwestern United States had the greatest suitable habitat, and this result differed from the 2006 model. Our work highlights the utility of iterative modeling for invasive species habitat modeling as new information becomes available.

Western United States↗

USGS2003

Incorporating precision, accuracy and alternative sampling designs into a continental monitoring program for colonial waterbirds

A comprehensive monitoring program for colonial waterbirds in North America has never existed. At smaller geographic scales, many states and provinces conduct surveys of colonial waterbird populations. Periodic regional surveys are conducted at varying times during the breeding season using a variety of survey methods, which complicates attempts to estimate population trends for most species. The US Geological Survey Patuxent Wildlife Research Center has recently started to coordinate colonial waterbird monitoring efforts throughout North America. A centralized database has been developed with an Internet-based data entry and retrieval page. The extent of existing colonial waterbird surveys has been defined, allowing gaps in coverage to be identified and basic inventories completed where desirable. To enable analyses of comparable data at regional or larger geographic scales, sampling populations through statistically sound sampling designs should supersede obtaining counts at every colony. Standardized breeding season survey techniques have been agreed upon and documented in a monitoring manual. Each survey in the manual has associated with it recommendations for bias estimation, and includes specific instructions on measuring detectability. The methods proposed in the manual are for developing reliable, comparable indices of population size to establish trend information at multiple spatial and temporal scales, but they will not result in robust estimates of total population numbers.

Ornis Hungarica↗

USGS2014

Monitoring conterminous United States (CONUS) land cover change with Web-Enabled Landsat Data (WELD)

Forest cover loss and bare ground gain from 2006 to 2010 for the conterminous United States (CONUS) were quantified at a 30 m spatial resolution using Web-Enabled Landsat Data available from the USGS Center for Earth Resources Observation and Science (EROS) (http://landsat.usgs.gov/WELD.php). The approach related multi-temporal WELD metrics and expert-derived training data for forest cover loss and bare ground gain through a decision tree classification algorithm. Forest cover loss was reported at state and ecoregional scales, and the identification of core forests' absent of change was made and verified using LiDAR data from the GLAS (Geoscience Laser Altimetry System) instrument. Bare ground gain correlated with population change for large metropolitan statistical areas (MSAs) outside of desert or semi-desert environments. GoogleEarth™ time-series images were used to validate the products. Mapped forest cover loss totaled 53,084 km2 and was found to be depicted conservatively, with a user's accuracy of 78% and a producer's accuracy of 68%. Excluding errors of adjacency, user's and producer's accuracies rose to 93% and 89%, respectively. Mapped bare ground gain equaled 5974 km2 and nearly matched the estimated area from the reference (GoogleEarth™) classification; however, user's (42%) and producer's (49%) accuracies were much less than those of the forest cover loss product. Excluding errors of adjacency, user's and producer's accuracies rose to 62% and 75%, respectively. Compared to recent 2001–2006 USGS National Land Cover Database validation data for forest loss (82% and 30% for respective user's and producer's accuracies) and urban gain (72% and 18% for respective user's and producer's accuracies), results using a single CONUS-scale model with WELD data are promising and point to the potential for national-scale operational mapping of key land cover transitions. However, validation results highlighted limitations, some of which can be addressed by improving training data, creating a more robust image feature space, adding contemporaneous Landsat 5 data to the inputs, and modifying definition sets to account for differences in temporal and spatial observational scales. The presented land cover extent and change data are available via the official WELD website (ftp://weldftp.cr.usgs.gov/CONUS_5Y_LandCover/ftp://weldftp.cr.usgs.gov/CONUS_5Y_LandCover/).

Remote Sensing of Environment↗

USGS2012

Fundamental questions of earthquake statistics, source behavior, and the estimation of earthquake probabilities from possible foreshocks

Estimates of the probability that an M L 4.8 earthquake, which occurred near the southern end of the San Andreas fault on 24 March 2009, would be followed by an M 7 mainshock over the following three days vary from 0.0009 using a Gutenberg–Richter model of aftershock statistics (Reasenberg and Jones, 1989) to 0.04 using a statistical model of foreshock behavior and long‐term estimates of large earthquake probabilities, including characteristic earthquakes (Agnew and Jones, 1991). I demonstrate that the disparity between the existing approaches depends on whether or not they conform to Gutenberg–Richter behavior. While Gutenberg–Richter behavior is well established over large regions, it could be violated on individual faults if they have characteristic earthquakes or over small areas if the spatial distribution of large‐event nucleations is disproportional to the rate of smaller events. I develop a new form of the aftershock model that includes characteristic behavior and combines the features of both models. This new model and the older foreshock model yield the same results when given the same inputs, but the new model has the advantage of producing probabilities for events of all magnitudes, rather than just for events larger than the initial one. Compared with the aftershock model, the new model has the advantage of taking into account long‐term earthquake probability models. Using consistent parameters, the probability of an M 7 mainshock on the southernmost San Andreas fault is 0.0001 for three days from long‐term models and the clustering probabilities following the M L 4.8 event are 0.00035 for a Gutenberg–Richter distribution and 0.013 for a characteristic‐earthquake magnitude–frequency distribution. Our decisions about the existence of characteristic earthquakes and how large earthquakes nucleate have a first‐order effect on the probabilities obtained from short‐term clustering models for these large events.

Bulletin of the Seismological Society of America↗

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↗