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At least 109 records · Page 6Linked to original sources
USGS2015

PESTools – A Python toolkit for processing PEST-related information

PESTools is an open-source Python package for processing and visualizing information associated with the parameter estimation software PEST and PEST++. While PEST output can be reformatted for post- processing in spreadsheets or other menu-driven software packages, that approach can be error-prone and time-consuming. Managing information from highly parameterized models with thousands of parameters and observations presents additional challenges. PESTools consists of a set of Python object classes to facilitate efficient processing and visualization of PEST-related information. Processing and visualization of observation residuals, objective function contributions, parameter and observation sensitivities, parameter correlation and identifiability, and other common PEST outputs have been implemented. PESTools is integrated with the pyemu software package for linear-based computer model uncertainty analyses, allowing for efficient computations using the Jacobian Matrix without any external utilities or files. The use of dataframe objects (pandas Python package) facilitates rapid subsetting and querying of large datasets, as well as the incorporation of ancillary information such as observation locations, times, measurement types, and other associated information. PESTools’ object methods can be easily scripted with concise code, or alternatively, the use of IPython notebooks allows for live interaction with the information. PESTools is designed to streamline workflows and provide deeper insight into model behavior, enhance troubleshooting, and improve transparency in the calibration process.

Conference Paper↗
USGS2019

Lewis River Bull Trout synthesis of known information

This document comprises a synthesis of bull trout information collected in the Lewis River for over two decades, and is the first such compilation of information in the subbasin since Graves (1982). We have summarized the information on demographic characteristics, vital rates, spatial distribution, movement patterns and genetic diversity in an effort to identify data gaps and research and monitoring needs for the future. This information will initially inform the development of a bull trout management plan for the Lewis River core area and will be a living document to capture additional information as it becomes available.

Washington↗
USGS2004

Integrating digital information for coastal and marine sciences

A pilot distributed geolibrary, the Marine Realms Information Bank (MRIB), was developed by the U.S. Geological Survey Coastal and Marine Geology Program and the Woods Hole Oceanographic Institution, to classify, integrate, and facilitate access to scientific information about oceans, coasts, and lakes. The MRIB is composed of a categorization scheme, a metadata database, and a specialized software backend, capable of drawing together information from remote sources without modifying their original format or content. Twelve facets are used to classify information: location, geologic time, feature type, biota, discipline, research method, hot topics, project, agency, author, content type, and file type. The MRIB approach allows easy and flexible organization of large or growing document collections for which centralized repositories would be impractical. Geographic searching based on the gazetteer and map interface is the centerpiece of the MRIB distributed geolibrary. The MRIB is one of a very few digital libraries that employ georeferencing -- a fundamentally different way to structure information from the traditional author/title/subject/keyword approach employed by most digital libraries. Lessons learned in developing the MRIB will be useful as other digital libraries confront the challenges of georeferencing.

Journal of Digital Information Management↗
USGS2022

Open removal models with temporary emigration and population dynamics to inform invasive animal management

Removal sampling data are the primary source of monitoring information for many populations (e.g., invasive species, fisheries). Population dynamics, temporary emigration, and imperfect detection are common sources of variation in monitoring data and are key parameters for informing management. We developed two open robust-design removal models for simultaneously modeling population dynamics, temporary emigration, and imperfect detection: a random walk linear trend model (estimable without ancillary information), and a 2-age class informed population model (InfoPM, closely related to integrated population models) that incorporated prior information for age-structured vital rates and relative juvenile availability. We applied both models to multiyear, removal trapping time-series of a large invasive lizard (Argentine black and white tegu, Salvator merianae ) in three management areas of South Florida to evaluate the effectiveness of management programs. Although estimates of the two models were similar, the InfoPMs generally returned more precise estimates, partitioned dynamics into births, deaths, net migration, and provided a decision support tool to predict population dynamics under different effort scenarios while accounting for uncertainty. Trends in tegu superpopulation abundance estimates were increasing in two management areas despite generally high removal rates. However, tegu abundance appeared to decline in the Core management area, where trapping density was the highest and immigration the lowest. Finally, comparing abundance predictions of no-removal scenarios to those estimated in each management area suggested significant population reductions due to management. These results suggest that local tegu population control via systematic trapping may be feasible with high enough trap density and limited immigration; and highlights the value of these trapping programs. We provided the first estimates of tegu abundance, capture probabilities, and population dynamics, which is critical for effective management. Furthermore, our models are applicable to a wide range of monitoring programs (e.g., carcass recovery or removal point-counts).

Ecology and Evolution↗
USGS2024

Comparing conventional tagging methods and acoustic telemetry to inform management of Lake Whitefish in Lake Michigan

Objective Studies of fish movement using conventional tags or acoustic telemetry have different benefits and biases that can influence how conclusions are used in a management context. Our objective was to determine whether these two methods provided similar inferences regarding movements and spawning site fidelity of Lake Whitefish Coregonus clupeaformis in Lake Michigan. Additionally, we assessed movement patterns and used telemetry to assess residency time of Lake Whitefish to provide managers with information on which stocks might be exposed to harvest in different regions. Methods Lake Whitefish were tagged during spawning in (1) North and Moonlight bays, (2) Big Bay de Noc, (3) the Menominee River, and (4) the Fox River. Proportions of fish moving between southern Green Bay, northern Green Bay, and Lake Michigan were compared between tag types. Spawning site fidelity was estimated for each tagging site. Seasonal residency indices were calculated using acoustic telemetry detections. Result Estimates differed between the two methods, but overall trends were similar. Fox River fish rarely left southern Green Bay, and fish tagged in North and Moonlight bays rarely entered Green Bay (<10% of individuals). Big Bay de Noc and Menominee River fish moved into other regions more often (>50% of individuals). The residency indices indicated that Big Bay de Noc fish spent most of their time in Lake Michigan while Menominee River fish spent little time in northern Green Bay despite transitioning to the region. Compared to telemetry, conventional tag recoveries underestimated the proportion of individuals moving among regions. Spawning site fidelity estimates (28–100%) varied among tagging groups and between methods. Conclusion Our results suggest that data from conventional tags can inform management at broad geographic scales. However, acoustic telemetry can provide fine-scale information. Information gained from telemetry can be useful in understanding exposure to fishing mortality, which may be valuable for informing management decisions.

Lake Michigan↗
USGS2021

Identifying information gaps in predicting winter foraging habitat for juvenile Gulf Sturgeon

The Gulf Sturgeon Acipenser oxyrinchus desotoi is an anadromous species that inhabits Gulf of Mexico coastal waters from Louisiana to Florida and is listed as threatened under the U.S. Endangered Species Act. Seasonal cues (e.g., freshwater discharge) determine the timing of spawning and migration and may influence the availability of critical habitat during winter months in six estuaries. Large information gaps, especially related to critical estuarine habitat for juveniles, hinder recovery efforts to protect these habitats and assess risks from emerging threats. Using Apalachicola Bay, Florida, as a model system, we developed and analyzed a preliminary Bayesian network model so that we could identify knowledge gaps (i.e., where expert knowledge was lacking) and data gaps (i.e., where data were unavailable) that limit the ability to assess the quantity of critical estuarine habitat for juvenile Gulf Sturgeon. The model hypothesized habitat availability per winter month in estuarine habitat under alternative scenarios of river discharge and length of the winter foraging season. A search for geospatial data sets revealed that the largest gap involved salinity, temperature, and oxygen (i.e., water condition) monitoring data, with data available only for Apalachicola Bay. For the Apalachicola Bay model, data gaps prevented the development of 53% of water condition geospatial data sets and a sensitivity analysis showed that water condition data most limited the ability to predict habitat availability. Expert knowledge was low, and conditional certainty scores showed that the relationships with the lowest certainty were abiotic suitability and habitat availability. Reducing information gaps could aid the development of a model that is appropriate for informing management. Future efforts could prioritize the expansion of water monitoring within critical habitat estuaries and predicting abiotic suitability and habitat availability. Bayesian network models can easily incorporate prior and new information for complex systems. Thus, our model could be updated as future research and monitoring efforts close these information gaps.

Florida↗
USGS2007

Predicting the potential distribution of invasive exotic species using GIS and information-theoretic approaches: A case of ragweed (Ambrosia artemisiifolia L.) distribution in China

Invasive exotic species pose a growing threat to the economy, public health, and ecological integrity of nations worldwide. Explaining and predicting the spatial distribution of invasive exotic species is of great importance to prevention and early warning efforts. We are investigating the potential distribution of invasive exotic species, the environmental factors that influence these distributions, and the ability to predict them using statistical and information-theoretic approaches. For some species, detailed presence/absence occurrence data are available, allowing the use of a variety of standard statistical techniques. However, for most species, absence data are not available. Presented with the challenge of developing a model based on presence-only information, we developed an improved logistic regression approach using Information Theory and Frequency Statistics to produce a relative suitability map. This paper generated a variety of distributions of ragweed (Ambrosia artemisiifolia L.) from logistic regression models applied to herbarium specimen location data and a suite of GIS layers including climatic, topographic, and land cover information. Our logistic regression model was based on Akaike's Information Criterion (AIC) from a suite of ecologically reasonable predictor variables. Based on the results we provided a new Frequency Statistical method to compartmentalize habitat-suitability in the native range. Finally, we used the model and the compartmentalized criterion developed in native ranges to "project" a potential distribution onto the exotic ranges to build habitat-suitability maps. ?? Science in China Press 2007.

Chinese Science Bulletin↗
USGS1987

Regional regression of flood characteristics employing historical information

Streamflow gauging networks provide hydrologic information for use in estimating the parameters of regional regression models. The regional regression models can be used to estimate flood statistics, such as the 100 yr peak, at ungauged sites as functions of drainage basin characteristics. A recent innovation in regional regression is the use of a generalized least squares (GLS) estimator that accounts for unequal station record lengths and sample cross correlation among the flows. However, this technique does not account for historical flood information. A method is proposed here to adjust this generalized least squares estimator to account for possible information about historical floods available at some stations in a region. The historical information is assumed to be in the form of observations of all peaks above a threshold during a long period outside the systematic record period. A Monte Carlo simulation experiment was performed to compare the GLS estimator adjusted for historical floods with the unadjusted GLS estimator and the ordinary least squares estimator. Results indicate that using the GLS estimator adjusted for historical information significantly improves the regression model.

Journal of Hydrology↗
USGS2006

Assimilation of snow covered area information into hydrologic and land-surface models

This paper describes a data assimilation method that uses observations of snow covered area (SCA) to update hydrologic model states in a mountainous catchment in Colorado. The assimilation method uses SCA information as part of an ensemble Kalman filter to alter the sub-basin distribution of snow as well as the basin water balance. This method permits an optimal combination of model simulations and observations, as well as propagation of information across model states. Sensitivity experiments are conducted with a fairly simple snowpack/water-balance model to evaluate effects of the data assimilation scheme on simulations of streamflow. The assimilation of SCA information results in minor improvements in the accuracy of streamflow simulations near the end of the snowmelt season. The small effect from SCA assimilation is initially surprising. It can be explained both because a substantial portion of snowmelts before any bare ground is exposed, and because the transition from 100% to 0% snow coverage occurs fairly quickly. Both of these factors are basin-dependent. Satellite SCA information is expected to be most useful in basins where snow cover is ephemeral. The data assimilation strategy presented in this study improved the accuracy of the streamflow simulation, indicating that SCA is a useful source of independent information that can be used as part of an integrated data assimilation strategy. ?? 2005 Elsevier Ltd. All rights reserved.

Advances in Water Resources↗
USGS2019

Integrating amphibian movement studies across scales better informs conservation decisions

Numerous papers have highlighted the need to integrate amphibian research and conservation across multiple scales. Despite this, most amphibian movement studies focus on a single level of organization (e.g., local population) and a single life stage (e.g., adults) and many suggest potential conservation actions or imply that the information is useful to conservation, yet these presumptions are rarely clarified or tested. Movement studies to date provide little information to guide conservation decisions directly because they fail to integrate movement across scales with individual or population parameters (i.e., fitness metrics); this is exacerbated by a general failure to set movement studies in a probabilistic context. An integrative approach allows prediction of population or metapopulation responses to environmental changes and different management actions, thus directly informing conservation decisions and ‘moving the needle’ towards an informed application of conservation actions. To support this perspective we: 1) revisit reviews of amphibian movement to illustrate the focus on single scales and to underscore the importance of movement – at all scales – to conservation; 2) make the case that movement, breeding, and other demographic probabilities are intertwined and studies executed at different temporal and spatial scales can aid in understanding species' responses to varying environmental and/or management conditions; 3) identify limitations of existing movement-related research to predict conservation action outcomes and inform decision-making; and 4) highlight under-utilized quantitative approaches that facilitate research that either connects movement to fitness metrics (individual-level studies) or estimates population and metapopulation vital rates in addition to, or associated with, movement probabilities.

Biological Conservation↗
USGS2011

Adaptive resource management and the value of information

The value of information is a general and broadly applicable concept that has been used for several decades to aid in making decisions in the face of uncertainty. Yet there are relatively few examples of its use in ecology and natural resources management, and almost none that are framed in terms of the future impacts of management decisions. In this paper we discuss the value of information in a context of adaptive management, in which actions are taken sequentially over a timeframe and both future resource conditions and residual uncertainties about resource responses are taken into account. Our objective is to derive the value of reducing or eliminating uncertainty in adaptive decision making. We describe several measures of the value of information, with each based on management objectives that are appropriate for adaptive management. We highlight some mathematical properties of these measures, discuss their geometries, and illustrate them with an example in natural resources management. Accounting for the value of information can help to inform decisions about whether and how much to monitor resource conditions through time.

Ecological Modelling↗
USGS2025

Monitoring visitor activity and informal trail disturbance in Yosemite Valley meadows to assess temporal changes in use and impacts

Montane meadows provide vital habitat that supports ecosystems, regulate hydrological processes, and offer valuable recreational opportunities. Meadows account for 3 % of Yosemite National Park's area, including Yosemite Valley, and are particularly susceptible to human impacts such as formation of informal trails. We collected observational data on visitor activity and quantified social trail disturbance to compare with resource monitoring datasets and through similar parameters to Walden-Schreiner and Leung (2013) who studied visitor use and behavior in three Yosemite Valley meadows in 2011. We documented change in number of visitors per hour and primary activity pursued. We also compared trends of meadow disturbance (informal trail length and disturbed area) from the early 2000s - 2023. Informal trail length at El Capitan Meadow decreased from 2004 to 2018, followed by recent increases. Disturbed area for Cooks A, Sentinel A, Slaughterhouse B, Stoneman A, and Stoneman B sub-meadows increased from 2006 to 2023. Between 2011 and 2023, the proportion of those engaged in active versus stationary pursuits showed that visitors engaged in more active pursuits in two of three meadows. Moreover, there were >3.5 times more visitors per hour in Cooks, El Capitan, and Leidig meadows in 2023 compared to 2011, yet, most visitors utilized designated trails. Meadow locational context was linked to activity preference and types of impacts. Management strategies, such as fencing and signage have been effective at minimizing impacts in several meadows. Parsing nuances of informal trail use and drivers of disturbance for various meadows is crucial for informed park management decisions and actions.

California↗
USGS2024

How to select an objective function using information theory

In machine learning or scientific computing, model performance is measured with an objective function. But why choose one objective over another? According to the information-theoretic paradigm, the “best” objective function is whichever minimizes information loss. To evaluate different objectives, transform them into likelihoods. The ratios of these likelihoods represent how strongly we should prefer one objective versus another, and the log of that ratio represents the relative information loss (or gain) from one objective to another. In plain terms, minimizing information loss is equivalent to minimizing uncertainty, as well as maximizing probability and general utility. We argue that this paradigm is well-suited to models that have many uses and no definite utility like the complex Earth system models used to understand the effects of climate change. Furthermore, the benefits of “maximizing information and general utility” extend beyond model accuracy to other important considerations including how efficiently the model calibrates, how well it generalizes, and how well it compresses data.

Water Resources Research↗
USGS2010

Incorporation of prior information on parameters into nonlinear regression groundwater flow models: 2. Applications

This paper investigates factors influencing the degree of improvement in estimates of parameters of a nonlinear regression groundwater flow model by incorporating prior information of unknown reliability. Consideration of expected behavior of the regression solutions and results of a hypothetical modeling problem lead to several general conclusions. First, if the parameters are properly scaled, linearized expressions for the mean square error (MSE) in parameter estimates of a nonlinear model will often behave very nearly as if the model were linear. Second, by using prior information, the MSE in properly scaled parameters can be reduced greatly over the MSE of ordinary least squares estimates of parameters. Third, plots of estimated MSE and the estimated standard deviation of MSE versus an auxiliary parameter (the ridge parameter) specifying the degree of influence of the prior information on regression results can help determine the potential for improvement of parameter estimates. Fourth, proposed criteria can be used to make appropriate choices for the ridge parameter and another parameter expressing degree of overall bias in the prior information. Results of a case study of Truckee Meadows, Reno-Sparks area, Washoe County, Nevada, conform closely to the results of the hypothetical problem. In the Truckee Meadows case, incorporation of prior information did not greatly change the parameter estimates from those obtained by ordinary least squares. However, the analysis showed that both sets of estimates are more reliable than suggested by the standard errors from ordinary least squares.

Water Resources Research↗
USGS2020

Using value of information to prioritize research needs for migratory bird management under climate change: A case study using federal land acquisition in the United States

In response to global habitat loss, many governmental and non‐governmental organizations have implemented land acquisition programs to protect critical habitats permanently for priority species. The ability of these protected areas to meet future management objectives may be compromised if the effects of climate change are not considered in acquisition decisions. Unfortunately, the effects of climate change on ecological systems are complex and plagued by uncertainty, making it difficult for organizations to prioritize research needs to improve decision‐making. Herein, we demonstrate the use of qualitative value of information analysis to identify and prioritize which sources of uncertainty should be reduced to improve land acquisition decisions to protect migratory birds in the face of climate change. The qualitative value of information analysis process involves four steps: ( i ) articulating alternative hypotheses; ( ii ) determining the magnitude of uncertainty regarding each hypothesis; ( iii ) evaluating the relevance of each hypothesis to acquisition decision‐making; and ( iv ) assessing the feasibility of reducing the uncertainty surrounding each hypothesis through research and monitoring. We demonstrate this approach using the objectives of 3 U.S. federal land acquisition programs that focus on migratory bird management. We used a comprehensive literature review, expert elicitation, and professional judgement to evaluate 11 hypotheses about the effect of climate change on migratory birds. Based on our results, we provide a list of priorities for future research and monitoring to reduce uncertainty and improve land acquisition decisions for the programs considered in our case study. Reducing uncertainty about how climate change will influence the spatial distribution of priority species and biotic homogenization were identified as the highest priorities for future research due to both the value of this information for improving land acquisition decisions and the feasibility of reducing uncertainty through research and monitoring. Research on how changes in precipitation patterns and winter severity will influence migratory bird abundance is also expected to benefit land acquisition decisions. By contrast, hypotheses about phenology and migration distance were identified as low priorities for research. By providing a rigorous and transparent approach to prioritizing research, we demonstrate that qualitative value of information is a valuable tool for prioritizing research and improving management decisions in other complex, high‐uncertainty cases where traditional quantitative value of information analysis is not possible. Given the inherent complexity of ecological systems under climate change, and the difficulty of identifying management‐relevant research priorities, we expect this approach to have wide applications within the field of natural resource management.

Biological Reviews↗
USGS2016

Fine‐resolution conservation planning with limited climate‐change information

Climate‐change induced uncertainties in future spatial patterns of conservation‐related outcomes make it difficult to implement standard conservation‐planning paradigms. A recent study translates Markowitz's risk‐diversification strategy from finance to conservation settings, enabling conservation agents to use this diversification strategy for allocating conservation and restoration investments across space to minimize the risk associated with such uncertainty. However, this method is information intensive and requires a large number of forecasts of ecological outcomes associated with possible climate‐change scenarios for carrying out fine‐resolution conservation planning. We developed a technique for iterative, spatial portfolio analysis that can be used to allocate scarce conservation resources across a desired level of subregions in a planning landscape in the absence of a sufficient number of ecological forecasts. We applied our technique to the Prairie Pothole Region in central North America. A lack of sufficient future climate information prevented attainment of the most efficient risk‐return conservation outcomes in the Prairie Pothole Region. The difference in expected conservation returns between conservation planning with limited climate‐change information and full climate‐change information was as large as 30% for the Prairie Pothole Region even when the most efficient iterative approach was used. However, our iterative approach allowed finer resolution portfolio allocation with limited climate‐change forecasts such that the best possible risk‐return combinations were obtained. With our most efficient iterative approach, the expected loss in conservation outcomes owing to limited climate‐change information could be reduced by 17% relative to other iterative approaches.

Conservation Biology↗
USGS2024

Computational approaches improve evidence synthesis and inform broad fisheries trends

Addressing ecological impacts with effective conservation actions requires information on the links between human pressures and localized responses. Understanding links is a priority for many conservation contexts, including the world's fresh waters, which face intensifying threats to disproportionately high species diversity, including more than half of the world's fish species. Literature synthesis can uncover links and highlight potential research gaps, yet can be very cumbersome and time consuming. Emerging tools like text mining can improve efficiency in extracting relevant information from vast scientific outputs. This study synthesizes evidence of direct anthropogenic threats to major inland fisheries and examines driver-impact-response patterns using coupled automated and manual text classification methods. We screened 9336 abstracts from 45 river basins of high importance to inland fish production; 1152 abstracts contained evidence of direct threats to fish. The most common documented drivers were pollution, dams, and fishing pressure, which were most strongly linked to decreased fitness, altered reproduction, and mortality, respectively. Strong impact-response links to pollution signal potential bias toward documenting acute threats that generate more visible and immediate impacts. The use of machine learning-based text classification performed best in classifying extraneous information. Results can inform the development of inland fisheries indicators and threat-based metrics, highlight possible evidence gaps in linking global drivers to fishery-level responses, and illustrate the application of a coupled synthesis approach for improved efficiency and extraction of information relevant to conservation outcomes. The associated user and interpretation guides address accessibility and technical barriers faced by conservation scientists to improve efficiency in evidence synthesis.

Conservation Science and Practice↗
USGS2022

Genomics-informed delineation of conservation units in a desert amphibian

Delineating conservation units (CUs, e.g., evolutionarily significant units, ESUs, and management units, MUs) is critical to the recovery of declining species because CUs inform both listing status and management actions. Genomic data have strengths and limitations in informing CU delineation and related management questions in natural systems. We illustrate the value of using genomic data in combination with landscape, dispersal, and occupancy data, to inform CU delineation in Nevada populations of the Great Basin Distinct Population Segment of the Columbia spotted frog ( Rana luteiventris ). R. luteiventris occupies naturally fragmented aquatic habitats in this xeric region, but beaver removal, climate change, and other factors have put many of these populations at high risk of extirpation without management intervention. We addressed three objectives: (1) assessing support for ESUs within Nevada; (2) evaluating and revising, if warranted, the current delineation of MUs; and (3) evaluating genetic diversity, effective population size, adaptive differentiation, and functional connectivity to inform ongoing management actions. We found little support for ESUs within Nevada but did identify potential revisions to MUs based on unique landscape drivers of connectivity that distinguish these desert populations from those in the northern portion of the species range. Effective sizes were uniformly small, with low genetic diversity and weak signatures of adaptive differentiation. Our findings suggest that management actions, including translocations and genetic rescue, might be warranted. Our study illustrates how a carefully planned genetic study, designed to address priority management goals that include CU delineation, can provide multiple insights to inform conservation action.

Molecular Ecology↗