Geology topics

Colin Talbert

Publications and source records attributed to Colin Talbert.

13 recordsLinked to original sources

USGS2018

Forecasting an invasive species’ distribution with global distribution data, local data, and physiological information

Understanding invasive species distributions and potential invasions often requires broad‐scale information on the environmental tolerances of the species. Further, resource managers are often faced with knowing these broad‐scale relationships as well as nuanced environmental factors related to their landscape that influence where an invasive species occurs and potentially could occur. Using invasive buffelgrass ( Cenchrus ciliaris ), we developed global models and local models for Saguaro National Park, Arizona, USA, based on location records and literature on physiological tolerances to environmental factors to investigate whether environmental relationships of a species at a global scale are also important at local scales. In addition to correlative models with five commonly used algorithms, we also developed a model using a priori user‐defined relationships between occurrence and environmental characteristics based on a literature review. All correlative models at both scales performed well based on statistical evaluations. The user‐defined curves closely matched those produced by the correlative models, indicating that the correlative models may be capturing mechanisms driving the distribution of buffelgrass. Given climate projections for the region, both global and local models indicate that conditions at Saguaro National Park may become more suitable for buffelgrass. Combining global and local data with correlative models and physiological information provided a holistic approach to forecasting invasive species distributions.

Arizona↗

USGS2017

Crossing boundaries in a collaborative modeling workspace

There is substantial literature on the importance of bridging across disciplinary and science–management boundaries. One of the ways commonly suggested to cross boundaries is for participants from both sides of the boundary to jointly produce information (i.e., knowledge co-production). But simply providing tools or bringing people together in the same room is not sufficient. Here we present a case study documenting the mechanisms by which managers and scientists collaborated to incorporate climate change projections into Colorado’s State Wildlife Action Plan. A critical component of the project was the use of a collaborative modeling and visualization workspace: the U.S. Geological Survey’s Resource for Advanced Modeling (RAM). Using video analysis and pre/post surveys from this case study, we examine how the RAM facilitated cognitive and social processes that co-produced a more salient and credible end product. This case provides practical suggestions to scientists and practitioners who want to implement actionable science.

Society and Natural Resources↗

USGS2017

Evaluating simplistic methods to understand current distributions and forecast distribution changes under climate change scenarios: An example with coypu ( Myocastor coypus )

Invasive species provide a unique opportunity to evaluate factors controlling biogeographic distributions; we can consider introduction success as an experiment testing suitability of environmental conditions. Predicting potential distributions of spreading species is not easy, and forecasting potential distributions with changing climate is even more difficult. Using the globally invasive coypu ( Myocastor coypus [Molina, 1782]), we evaluate and compare the utility of a simplistic ecophysiological based model and a correlative model to predict current and future distribution. The ecophysiological model was based on winter temperature relationships with nutria survival. We developed correlative statistical models using the Software for Assisted Habitat Modeling and biologically relevant climate data with a global extent. We applied the ecophysiological based model to several global circulation model ( GCM ) predictions for mid-century. We used global coypu introduction data to evaluate these models and to explore a hypothesized physiological limitation, finding general agreement with known coypu distribution locally and globally and support for an upper thermal tolerance threshold. Global circulation model based model results showed variability in coypu predicted distribution among GCMs , but had general agreement of increasing suitable area in the USA. Our methods highlighted the dynamic nature of the edges of the coypu distribution due to climate non-equilibrium, and uncertainty associated with forecasting future distributions. Areas deemed suitable habitat, especially those on the edge of the current known range, could be used for early detection of the spread of coypu populations for management purposes. Combining approaches can be beneficial to predicting potential distributions of invasive species now and in the future and in exploring hypotheses of factors controlling distributions.

NeoBiota↗

USGS2017

Minimizing effects of methodological decisions on interpretation and prediction in species distribution studies: An example with background selection

Evaluating the conditions where a species can persist is an important question in ecology both to understand tolerances of organisms and to predict distributions across landscapes. Presence data combined with background or pseudo-absence locations are commonly used with species distribution modeling to develop these relationships. However, there is not a standard method to generate background or pseudo-absence locations, and method choice affects model outcomes. We evaluated combinations of both model algorithms (simple and complex generalized linear models, multivariate adaptive regression splines, Maxent, boosted regression trees, and random forest) and background methods (random, minimum convex polygon, and continuous and binary kernel density estimator (KDE)) to assess the sensitivity of model outcomes to choices made. We evaluated six questions related to model results, including five beyond the common comparison of model accuracy assessment metrics (biological interpretability of response curves, cross-validation robustness, independent data accuracy and robustness, and prediction consistency). For our case study with cheatgrass in the western US, random forest was least sensitive to background choice and the binary KDE method was least sensitive to model algorithm choice. While this outcome may not hold for other locations or species, the methods we used can be implemented to help determine appropriate methodologies for particular research questions.

Ecological Modelling↗

USGS2016

Integrating remote sensing with species distribution models; Mapping tamarisk invasions using the Software for Assisted Habitat Modeling (SAHM)

Early detection of invasive plant species is vital for the management of natural resources and protection of ecosystem processes. The use of satellite remote sensing for mapping the distribution of invasive plants is becoming more common, however conventional imaging software and classification methods have been shown to be unreliable. In this study, we test and evaluate the use of five species distribution model techniques fit with satellite remote sensing data to map invasive tamarisk ( Tamarix spp.) along the Arkansas River in Southeastern Colorado. The models tested included boosted regression trees (BRT), Random Forest (RF), multivariate adaptive regression splines (MARS), generalized linear model (GLM), and Maxent. These analyses were conducted using a newly developed software package called the Software for Assisted Habitat Modeling (SAHM). All models were trained with 499 presence points, 10,000 pseudo-absence points, and predictor variables acquired from the Landsat 5 Thematic Mapper (TM) sensor over an eight-month period to distinguish tamarisk from native riparian vegetation using detection of phenological differences. From the Landsat scenes, we used individual bands and calculated Normalized Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), and tasseled capped transformations. All five models identified current tamarisk distribution on the landscape successfully based on threshold independent and threshold dependent evaluation metrics with independent location data. To account for model specific differences, we produced an ensemble of all five models with map output highlighting areas of agreement and areas of uncertainty. Our results demonstrate the usefulness of species distribution models in analyzing remotely sensed data and the utility of ensemble mapping, and showcase the capability of SAHM in pre-processing and executing multiple complex models.

Journal of Visualized Experiments↗

USGS2016

Development of a decision support tool for water and resource management using biotic, abiotic, and hydrological assessments of Topock Marsh, Arizona

Topock Marsh is a large wetland adjacent to the Colorado River and the main feature of Havasu National Wildlife Refuge (Havasu NWR) in southern Arizona. In 2010, the U.S. Fish and Wildlife Service (FWS) and Bureau of Reclamation began a project to improve water management capabilities at Topock Marsh and protect habitats and species. Initial construction required a drawdown, which caused below-average inflows and water depths in 2010–11. U.S. Geological Survey Fort Collins Science Center (FORT) scientists collected an assemblage of biotic, abiotic, and hydrologic data from Topock Marsh during the drawdown and immediately after, thus obtaining valuable information needed by FWS. Building upon that work, FORT developed a decision support system (DSS) to better understand ecosystem health and function of Topock Marsh under various hydrologic conditions. The DSS was developed using a spatially explicit geographic information system package of historical data, habitat indices, and analytical tools to synthesize outputs for hydrologic time periods. Deliverables include high-resolution orthorectified imagery of Topock Marsh; a DSS tool that can be used by Havasu NWR to compare habitat availability associated with three hydrologic scenarios (dry, average, wet years); and this final report which details study results. This project, therefore, has addressed critical FWS management questions by integrating ecologic and hydrologic information into a DSS framework. This DSS will assist refuge management to make better informed decisions about refuge operations and better understand the ecological results of those decisions by providing tools to identify the effects of water operations on species-specific habitat and ecological processes. While this approach was developed to help FWS use the best available science to determine more effective water management strategies at Havasu NWR, technologies used in this study could be applied elsewhere within the region.

Arizona↗

USGS2016

Evaluating methods to establish habitat suitability criteria: A case study in the upper Delaware River Basin, USA

Defining habitat suitability criteria (HSC) of aquatic biota can be a key component to environmental flow science. HSC can be developed through numerous methods; however, few studies have evaluated the consistency of HSC developed by different methodologies. We directly compared HSC for depth and velocity developed by the Delphi method (expert opinion) and by two primary literature meta-analyses (literature-derived range and interquartile range) to assess whether these independent methods produce analogous criteria for multiple species (rainbow trout, brown trout, American shad, and shallow fast guild) and life stages. We further evaluated how these two independently developed HSC affect calculations of habitat availability under three alternative reservoir management scenarios in the upper Delaware River at a mesohabitat (main channel, stream margins, and flood plain), reach, and basin scale. In general, literature-derived HSC fell within the range of the Delphi HSC, with highest congruence for velocity habitat. Habitat area predicted using the Delphi HSC fell between the habitat area predicted using two literature-derived HSC, both at the basin and the site scale. Predicted habitat increased in shallow regions (stream margins and flood plain) using literature-derived HSC while Delphi-derived HSC predicted increased channel habitat. HSC generally favoured the same reservoir management scenario; however, no favoured reservoir management scenario was the most common outcome when applying the literature range HSC. The differences found in this study lend insight into how different methodologies can shape HSC and their consequences for predicted habitat and water management decisions. Published 2016. This article is a U.S. Government work and is in the public domain in the USA.

New York, Pennsylvania↗

USGS2015

Community for Data Integration 2013 Annual Report

The U.S. Geological Survey (USGS) conducts earth science to help address complex issues affecting society and the environment. In 2006, the USGS held the first Scientific Information Management Workshop to bring together staff from across the organization to discuss the data and information management issues affecting the integration and delivery of earth science research and investigate the use of “communities of practice” as mechanisms to share expertise about these issues. Out of this effort emerged the Council for Data Integration, which was conceived as an official organizational function that would help guide data integration activities and formalize communities of practice into working groups. However by 2009, it became apparent that many members of the council had an interest in developing data integration solutions and sharing expertise in a less formal grassroots perspective, thus transforming the “Council” into a “Community” for Data Integration (CDI). Today, the CDI represents a dynamic community of practice focused on advancing science data and information management and integration capabilities across the USGS and the CDI community. The CDI fosters an environment for collaboration and sharing by bringing together expertise from external partners and representatives across USGS who are involved in research, data management, and information technology. Membership is voluntary and open to USGS employees and other individuals and organizations willing to contribute to the community (if interested, contact cdi@usgs.gov). The purpose of the CDI is to advance understanding of Earth systems through enhanced use of data and information including associated tools and techniques provide a forum for people doing work with data integration to come together to share ideas as well as learn new skills and techniques, and grow overall USGS capabilities with data and information by increasing visibility of the work of many people throughout the USGS and the CDI community. To achieve these goals, the CDI operates within four applied areas: monthly forums, annual workshop/webinar series, working groups, and projects. The monthly forums, also known as the Opportunity/Challenge of the Month, provide an open dialogue to share and learn about data integration efforts or to present problems that invite the Community to offer solutions, advice, and support. Since 2010, the CDI has also sponsored annual workshops/webinar series to encourage the exchange of ideas, sharing of activities, presentations of current projects, and networking among members. Stemming from common interests, the working groups are focused on efforts to address data management and technical 2 challenges, including the development of standards and tools, improving interoperability and information infrastructure, and data preservation within USGS and its partners. The growing support for the activities of the working groups led to the CDI’s first formal request for proposals (RFP) process in 2013 to fund projects that produced tangible products. Today the CDI continues to hold an annual RFP that create data management tools and practices, collaboration tools, and training in support of data integration and delivery.

Open-File Report↗

USGS2014

User's manual for the upper Delaware River riverine environmental flow decision support system (REFDSS), Version 1.1.2

Between 2002 and 2006, the Fort Collins Science Center (FORT) at the U.S. Geological Survey (USGS) conducted field surveys, organized workshops, and performed analysis of habitat for trout and shad in the Upper Delaware River Basin. This work culminated in the development of decision support system software (the Delaware River DSS–DRDSS, Bovee and others, 2007) that works in conjunction with the Delaware River Basin Commission’s reservoir operations model, OASIS, to facilitate comparison of the habitat and water-delivery effects of alternative operating scenarios for the Basin. This original DRDSS application was developed in Microsoft Excel and is available to all interested parties through the FORT web site ( http://www.fort.usgs.gov/Products/Software/DRDSS/ ). Initial user feedback on the original Excel-based DSS highlighted the need for a more user-friendly and powerful interface to effectively deliver the complex data and analyses encapsulated in the DSS. In order to meet this need, the USGS FORT and Northern Appalachian Research Branch (NARB) developed an entirely new graphical user interface (GUI) application. Support for this research was through the DOI WaterSmart program ( http://www.doi.gov/watersmart/html/index.php ) of which the USGS component is the National Water Census ( http://water.usgs.gov/watercensus/WaterSMART.html ). The content and methodology of the new GUI interface emulates those of the original DSS with a few exceptions listed below. Refer to Bovee and others (2007) for the original information. Significant alterations to the original DSS include: • We moved from Excel-based data storage and processing to a more powerful database back end powered by SQLite. The most notable effect of this is that the previous maximum temporal extent of 10 years has been replaced by a dynamic extent that can now cover the entire period of record for which we have data (1928–2000). • We incorporated interactive geographic information system (GIS) visualization and dynamic data processing. Previous habitat maps were generated outside of the DSS in an ad hoc process that the end user could not update or investigate. • The original bathymetric data collected in 2005 at the three main stem reaches was augmented with a higher resolution dataset collected in 2010. This new dataset was collected in order to conduct higher resolution (finer pixel size) two-dimensional (2D) hydrodynamic modeling for evaluating dwarf wedgemussel (DWM, Alasmidonta heterodon ) habitat. • Results charts are now substantially more interactive, dynamic, and accessible, which allows users to more easily focus on their particular topics of interest as well as drill down to the source data used to calculate given results.

Open-File Report↗

USGS2013

MODIS phenology image service ArcMap toolbox

Seasonal change is important to consider when managing conservation areas at landscape scales. The study of such patterns throughout the year is referred to as phenology. Recurring life-cycle events that are initiated and driven by environmental factors include animal migration and plant flowering. Phenological events capture public attention, such as fall color change in deciduous forests, the first flowering in spring, and for those with allergies, the start of the pollen season. These events can affect our daily lives, provide clues to help understand and manage ecosystems, and provide evidence of how climate variability can affect the natural cycle of plants and animals. Phenological observations can be gathered at a range of scales, from plots smaller than an acre to landscapes of hundreds to thousands of acres. Linking these observations to diverse disciplines such as evolutionary biology or climate sciences can help further research in species and ecosystem responses to climate change scenarios at appropriate scales. A cooperative study between the National Park Service (NPS), the U.S. Geological Survey (USGS), and the National Aeronautics and Space Administration (NASA) has been exploring how satellite information can be used to summarize phenological patterns observed at the park or landscape scale and how those summaries can be presented to both park managers and visitors. This study specifically addressed seasonal changes in plants, including the onset of growth, photosynthesis in the spring, and the senescence of deciduous vegetation in the fall. The primary objective of the work is to demonstrate that seasonality even in protected areas changes considerably across years. A major challenge is to decouple natural variability from possible trends—directional change that can lead to a permanent and radically different ecosystem state. Trends can be either a gradual degradation of the landscape (often from external influences) or steady improvement (by implementing long-term conservation plans). In either case, it is important to first grasp the magnitude of natural variation so that it is not confused with actual trends. This work used existing and freely available remote sensing data, specifically the NASA-funded 250-meter (m) spatial resolution land-surface phenology product for North America. This product is calculated from an annual record of vegetation health observed by NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) instrument. The land-surface phenology product is, in essence, a method to summarize all the observations throughout a year into a few key, ecologically relevant “metrics”.

Open-File Report↗

USGS2013

Data management challenges in species distribution modeling

An important component in the fields of ecology and conservation biology is understanding the environmental conditions and geographic areas that are suitable for a given species to inhabit. A common tool in determining such areas is species distribution modeling which uses computer algorithms to determine the spatial distribution of organisms. Most commonly the correlative relationships between the organism and environmental variables are the primary consideration. The data requirements for this type of modeling consist of known presence and possibly absence locations of the species as well as the values of environmental or climatic covariates thought to define the species habitat suitability at these locations. These covariate data are generally extracted from remotely sensed imagery, interpolated/gridded historical climate data, or downscaled climate model output. Traditionally, ecologists and biologists have constructed species distribution models using workflows and data that reside primarily on their local workstations or networks. This workflow is becoming challenging as scientists increasingly try to use these modeling techniques to inform management decisions under different climate change scenarios. This challenge stems from the fact that remote sensing products, gridded historical climate, and downscaled climate models are not only increasing in spatial and temporal resolution but proliferating as well. Any rigorous assessment of uncertainty requires a computationally intensive sensitivity analysis accounting for various sources of uncertainty. The scientists fitting these models generally do not have the background in computer science required to take advantage of recent advances in web-service based data acquisition, remote high-powered data processing, or scientific workflow systems. Ecologists in the field of modeling are in need of a tractable platform that abstracts the inherent computational complexity required to incorporate the burgeoning field of coupled climate and ecological response modeling. In this paper we describe the computational challenges in species distribution modeling and solutions using scientific workflow systems. We focus on the Software for Assisted Species Modeling (SAHM) a package within VisTrails, an open-source scientific workflow system.

Bulletin of the Technical Committee on Data Engine↗

USGS2009

Decision Support System for Evaluation of Gunnison River Flow Regimes With Respect To Resources of the Black Canyon of the Gunnison National Park

This report describes and documents a decision support system for the Gunnison River in Black Canyon of the Gunnison National Park. It is a macro-embedded EXCEL program that calculates and displays indicators representing valued characteristics or processes in the Black Canyon based on daily flows of the Gunnison River. The program is designed to easily accept input from downloaded stream gage records or output from the RIVERWARE reservoir operations model being used for the upstream Aspinall Unit. The decision support system is structured to compare as many as eight alternative flow regimes, where each alternative is represented by a daily sequence of at least 20 calendar years of streamflow. Indicators include selected flow statistics, riparian plant community distribution, clearing of box elder by inundation and scour, several measures of sediment mobilization, trout fry habitat, and federal reserved water rights. Calculation of variables representing National Park Service federal reserved water rights requires additional secondary input files pertaining to forecast and actual basin inflows and storage levels in Blue Mesa reservoir. Example input files representing a range of situations including historical, reconstructed natural, and simulated alternative reservoir operations are provided with the software.

Open-File Report↗

USGS2008

Development and Application of a Decision Support System for Water Management Investigations in the Upper Yakima River, Washington

The Yakima River Decision Support System (YRDSS) was designed to quantify and display the consequences of different water management scenarios for a variety of state variables in the upper Yakima River Basin, located in central Washington. The impetus for the YRDSS was the Yakima River Basin Water Storage Feasibility Study, which investigated alternatives for providing additional water in the basin for threatened and endangered fish, irrigated agriculture, and municipal water supply. The additional water supplies would be provided by combinations of water exchanges, pumping stations, and off-channel storage facilities, each of which could affect the operations of the Bureau of Reclamation's (BOR) five headwaters reservoirs in the basin. The driver for the YRDSS is RiverWare, a systems-operations model used by BOR to calculate reservoir storage, irrigation deliveries, and streamflow at downstream locations resulting from changes in water supply and reservoir operations. The YRDSS uses output from RiverWare to calculate and summarize changes at 5 important flood plain reaches in the basin to 14 state variables: (1) habitat availability for selected life stages of four salmonid species, (2) spawning-incubation habitat persistence, (3) potential redd scour, (4) maximum water temperatures, (5) outmigration for bull trout (Salvelinus confluentus) from headwaters reservoirs, (6) outmigration of salmon smolts from Cle Elum Reservoir, (7) frequency of beneficial overbank flooding, (8) frequency of damaging flood events, (9) total deliverable water supply, (10) total water supply deliverable to junior water rights holders, (11) end-of-year reservoir carryover, (12) potential fine sediment transport rates, (13) frequency of events capable of armor layer disruption, and (14) geomorphic work performed during each water year. Output of the YRDSS consists of a series of conditionally formatted scoring tables, wherein the changes to a state variable resulting from an operational scenario are compiled and summarized. Increases in the values for state variables result in their respective backgrounds to turn green in the scoring matrix, whereas decreases in the values for state variables result in their respective backgrounds turning red. This convention was designed to provide decision makers with a quick visual assessment of the overall results of an operating scenario. An evaluation matrix and a variety of weighting strategies to reflect the relative importance of different state variables are also presented as options for further distillation of YRDSS results during the decision-making process.

Open-File Report↗