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At least 1,009 records · Page 56Linked to original sources
USGS2015

Preserving geomorphic data records of flood disturbances

No central database or repository is currently available in the USA to preserve long-term, spatially extensive records of fluvial geomorphic data or to provide future accessibility. Yet, because of their length and continuity these data are valuable for future research. Therefore, we built a public accessible website to preserve data records of two examples of long-term monitoring (40 and 18 years) of the fluvial geomorphic response to natural disturbances. One disturbance was ∼50-year flood on Powder River in Montana in 1978, and the second disturbance was a catastrophic flood on Spring Creek following a ∼100-year rainstorm after a wildfire in Colorado in 1996. Two critical issues arise relative to preserving fluvial geomorphic data. The first is preserving the data themselves, but the second, and just as important, is preserving information about the location of the field research sites where the data were collected so the sites can be re-located and re-surveyed in the future. The latter allows long-term datasets to be extended into the future and to provide critical background data for interpreting future landscape changes. Data were preserved on a website to allow world-wide accessibility and to upload new data to the website as they become available. We describe the architecture of the website, lessons learned in developing the website, future improvements, and recommendations on how also to preserve information about the location of field research sites.

GeoResJ↗
USGS2018

An introduction and practical guide to use of the Soil-Vegetation Inventory Method (SVIM) data

Long-term vegetation dynamics across public rangelands in the western United States are not well understood because of the lack of large-scale, readily available historic datasets. The Bureau of Land Management’s Soil-Vegetation Inventory Method (SVIM) program was implemented between 1977 and 1983 across 14 western states, but the data have not been easily accessible. We introduce the SVIM vegetation cover dataset in a georeferenced, digital format; summarize how the data were collected; and discuss potential limitations and biases. We demonstrate how SVIM data can be compared with contemporary monitoring datasets to quantify changes in vegetation associated with wildfire and the abundance of exotic invasive species. Specifically, we compare SVIM vegetation cover data with cover data collected by BLM’s Assessment, Inventory, and Monitoring (AIM) program (2011–2016) in a focal area in the northern Great Basin. We address issues associated with analyzing and interpreting data from these distinct programs, including differences in survey methods and potential biases introduced by spatial and temporal variation in sampling. We compared SVIM and AIM survey methods at 44 plots and found that percent cover estimates had high correspondence for all measured functional groups. Comparisons between historic SVIM data and recent AIM data documented significant declines in the occupancy and cover of native shrubs and native perennial forbs , and a significant increase in exotic annual forbs. Wildfire was a driver of change for some functional groups, with greater change occurring in AIM plots that burned between the two time periods compared with those that did not. Our results are consistent with previous studies showing that many native shrub-dominated plant communities in the Great Basin have been replaced by exotic annuals. Our study demonstrates that SVIM data will be an important resource for researchers interested in quantifying vegetation change through time across public rangelands in the western United States.

Rangeland Ecology and Management↗
USGS2006

Optimal interpolation analysis of leaf area index using MODIS data

A simple data analysis technique for vegetation leaf area index (LAI) using Moderate Resolution Imaging Spectroradiometer (MODIS) data is presented. The objective is to generate LAI data that is appropriate for numerical weather prediction. A series of techniques and procedures which includes data quality control, time-series data smoothing, and simple data analysis is applied. The LAI analysis is an optimal combination of the MODIS observations and derived climatology, depending on their associated errors σ o and σ c . The “best estimate” LAI is derived from a simple three-point smoothing technique combined with a selection of maximum LAI (after data quality control) values to ensure a higher quality. The LAI climatology is a time smoothed mean value of the “best estimate” LAI during the years of 2002–2004. The observation error is obtained by comparing the MODIS observed LAI with the “best estimate” of the LAI, and the climatological error is obtained by comparing the “best estimate” of LAI with the climatological LAI value. The LAI analysis is the result of a weighting between these two errors. Demonstration of the method described in this paper is presented for the 15-km grid of Meteorological Service of Canada (MSC)'s regional version of the numerical weather prediction model. The final LAI analyses have a relatively smooth temporal evolution, which makes them more appropriate for environmental prediction than the original MODIS LAI observation data. They are also more realistic than the LAI data currently used operationally at the MSC which is based on land-cover databases.

Remote Sensing of Environment↗
USGS2003

Complications with using ratios for environmental data: Comparing enantiomeric ratios (ERs) and enantiomer fractions (EFs)

Complications arise when ratios are used to present environmental data because ratios are an unbounded, multiplicative scale that can lead to asymmetrical (skewed) data distributions. Enantiomeric ratios (ERs), historically used in discussions of chiral signatures, often are published as mean ER??single-value standard deviation. Application of statistical summaries, such as the widely used sample mean and standard deviation, to skewed ratio data is misleading and often inappropriate. Comparison of statistically summarized ER and enantiomer fraction (EF) data (which are based on a bounded, additive scale) for a range of hypothetical values reveals substantial discrepancies when conversion between ER and EF formats is used. These discrepancies are largest when the ratio data are greater than one and have large variability, because the data are more skewed. In many cases, the use of fractions instead of ratios can help to minimize misrepresentation of environmental data, including chiral data. The use of nonparametric statistical summaries, e.g., median and percentiles, provides a more robust indicator of the typical value and spread for both ER and EF data. ?? 2003 Elsevier Ltd. All rights reserved.

Chemosphere↗
USGS2002

Effect of censoring trace-level water-quality data on trend-detection capability

Monte Carlo experiments were used to evaluate whether trace-level water-quality data that are routinely censored (not reported) contain valuable information for trend detection. Measurements are commonly censored if they fall below a level associated with some minimum acceptable level of reliability (detection limit). Trace-level organic data were simulated with best- and worst-case estimates of measurement uncertainty, various concentrations and degrees of linear trend, and different censoring rules. The resulting classes of data were subjected to a nonparametric statistical test for trend. For all classes of data evaluated, trends were most effectively detected in uncensored data as compared to censored data even when the data censored were highly unreliable. Thus, censoring data at any concentration level may eliminate valuable information. Whether or not valuable information for trend analysis is, in fact, eliminated by censoring of actual rather than simulated data depends on whether the analytical process is in statistical control and bias is predictable for a particular type of chemical analyses.

Environmental Science & Technology↗
USGS2019

PaCTS 1.0: A crowdsourced reporting standard for paleoclimate data

The progress of science is tied to the standardization of measurements, instruments, and data. This is especially true in the Big Data age, where analyzing large data volumes critically hinges on that data being standardized. Accordingly, the lack of community-sanctioned data standards in paleoclimatology has largely precluded the benefits of Big Data advances in the field. Building upon recent efforts to standardize the format and terminology of paleoclimate data, this article describes the Paleoclimate Community reporTing Standard (PaCTS), a crowdsourced reporting standard for such data. PaCTS captures which information should be included when reporting paleoclimate data, with the goal of maximizing the re-use value of paleoclimate datasets, particularly for synthesis work and comparison to climate model simulations. Initiated by the LinkedEarth project, this standard elicitation process involved an international workshop in 2016, various forms of digital community engagement over the next few years, and grassroots working groups. Participants in this process identified important properties across paleoclimate archives, in addition to the reporting of uncertainties and chronologies; they also identified archive-specific properties and distinguished reporting standards for new vs. legacy datasets. This work shows that at least 135 respondents overwhelmingly support a drastic increase in the amount of metadata accompanying paleoclimate datasets. Since such goals are at odds with present practices, we discuss a transparent path towards implementing or revising these recommendations in the near future, using both bottom-up and top-down approaches.

Paleoceanography and Paleoclimatology↗
USGS2025

Toward a new framework to evaluate process-based model configurations and quantify data worth prior to calibration

Model criticism, discrimination, and selection methods often rely on calibrated model outputs. Because calibration can be computationally expensive, model criticism can first be undertaken by assessing model outputs obtained from limited prior parameter ensembles. However, such prior-based methods are often heuristic and do not formalize the notion of balancing model consistency with data and model complexity (i.e., model adequacy). We present a new framework to discriminate among candidate models prior to calibration that formalizes prior-to-calibration model adequacy into a metric to implicitly balance prior model output data coverage with model complexity represented by prior output (co)variance. The prior model adequacy metric “Mahalanobis distance deviation” quantifies the deviation of (a) the set of squared Mahalanobis distances of data from a prior model output distribution from (b) the set of squared Mahalanobis distances of data from their own distribution. A new data worth metric “discernment value” is also presented which quantifies the value of data for screening less-adequate models prior to calibration. Discernment value is calculated from the change in variance of a weighted average of prior model outputs from all candidate models due to less-adequate model outputs receiving lower weight. The framework is demonstrated using a one-dimensional groundwater flow model with eight possible configurations. A synthetic data network is used to test the framework. Results show the framework identifies the candidate models most similar to the true model used to create the synthetic data. Discernment values show variation in the value of different data types and locations for screening less-adequate models.

Water Resources Research↗
USGS2026

Evaluating SWOT satellite data with gage information in the United States and Canada

The Surface Water and Ocean Topography (SWOT) satellite mission represents a transformative approach to water resources management, offering high-resolution data on water-surface elevations (WSE) and extents. This study evaluates SWOT WSE accuracy in the United States and Canada with in situ streamgage and lake and reservoir gage data. We compare SWOT Level 2 Hydrology data products (Version D) to in situ timeseries of WSE on 335,531 paired observations across 2,629 gages. SWOT data accuracy and temporal coverage are explored as functions of various SWOT quality filters. After applying a standard set of defined filters and thresholds to compare paired measurements of SWOT and in situ observations (both referenced to the Earth Gravitational Model 2008), we find 1-σ (68th percentile) absolute differences of 20 and 11 cm, and median biases of 2.8 and 0.1 cm, for filtered river node and lake data, respectively. River reach and lake comparisons may be biased due to scale mismatches between in situ point-based data and spatially averaged SWOT data. Applying SWOT data quality filters improved median paired elevation R2 between unfiltered and filtered data from 0.12 to 0.92 across all reach, node, and lake observations. Overall, we find that 60.0% and 73.8% of all paired SWOT node and lake WSE observations that satisfy SWOT-observable criteria (ice-free observations from sufficiently large waterbodies within the SWOT cross track range) have elevations within 25 cm of reported gage observations. These comparisons also provide a baseline benchmark that can help inform continued SWOT algorithm refinement.

Water Resources Research↗
USGS2012

Adjustment of total suspended solids data for use in sediment studies

The U.S. Environmental Protection Agency identifies fluvial sediment as the single most widespread pollutant in the Nation's rivers and streams, affecting aquatic habitat, drinking water treatment processes, and recreational uses of rivers, lakes, and estuaries. A significant amount of suspended-sediment data has been produced using the total suspended solids (TSS) laboratory analysis method. An evaluation of data collected and analyzed by the U.S. Geological Survey and others has shown that the variation in TSS analytical results is considerably larger than that for traditional suspended-sediment concentration analyses (SSC) and that the TSS data show a negative bias when compared to SSC data. This paper presents the initial results of a continuing investigation into the differences between TSS and SSC results. It explores possible relations between these differences and other hydrologic data collected at the same stations. A general equation was developed to relate TSS data to SSC data. However, this general equation is not applicable for data from individual stations. Based on these analyses, there appears to be no simple, straightforward way to relate TSS and SSC data unless pairs of TSS and SSC results are available for a station.

Conference Paper↗
USGS1998

North American landscape characterization project: The production of a continental scale three-decade Landsat data set

The North American Landscape Characterization (NALC) project is a component of the National Aeronautics and Space Administration (NASA) Landsat Pathfinder program. Pathfinder projects are focused on the investigation of global change utilizing current remote sensing technologies. The NALC project is a cooperative effort between the U. S. Environmental Protection Agency (EPA), the U.S. Geological Survey (USGS), and NASA to make Landsat data available to the widest possible user community for scientific research and general public interest. The NALC project is principally funded by the EPA Office of Research and Development and the USGS's Earth Resources Observation Systems (EROS) Data Center (EDC). The objectives of the NALC project are to produce standardized remote sensing data sets, develop standardized analysis methods, and derive standardized land cover change products for a large portion of the North American continent (the conterminous United States and Mexico) (Lunetta and Sturdevant, 1993). The standard product is the NALC “triplicate”;, consisting of co‐registered Landsat multispectral scanner data for the years 1973, 1986, and 1991 (plus or minus one year), plus co‐registered 3 arcsecond digital terrain elevation data. Processing began with the 1986 scene, which was precision corrected (with full terrain correction) to a 60 meter Universal Transverse Mercator base. Automated cross‐correlation procedures were used to co‐register the 1970's and 1990's data to the 1980's base, and independent verifications of registration quality were performed on all triplicate components. The pertinent metadata were compiled in a relational database, which includes WRS2 path/rows, scene ID's, image dates, solar azimuth and elevation, verification RMSE's, and the number of verification control points. NALC triplicate data sets are being used for a number of applications, including the analysis of urbanization patterns, dynamics of climatic fluctuations, deforestation studies, and vegetation classification and mapping. These data are being distributed through the Earth Observing System Data and Information System (EOSDIS) Information Management System (IMS) at a cost of $15(U.S.) for each triplicate.

Geocarto International↗
USGS2026

Subsurface structure across the Tacoma Basin, Washington State, using trans-dimensional Bayesian inversion of fundamental mode spatial autocorrelation data

Spatial autocorrelation (SPAC), the azimuthal average of the normalized cross-correlation between equidistant station pairs deployed in a 2-D array, is widely used to image the subsurface structure. However, the rigorous estimate of subsurface structure and its uncertainties as a function of depth using SPAC data is challenging due to the nonlinear relation between the SPAC data and Earth structure as well as the trade-off between depth and velocity. Additionally, data noise is strongly correlated due to data processing (e.g. filtering, stacking from multiple time segments and azimuthal averaging). Most studies do not account for the correlated noise and fix the ratio of compressional-wave velocity ( V P ) to shear-wave velocity ( V s ) (i.e. V P / V s ratio) and the number of layers, both of which are typically unknown. To address these challenges, we develop a hierarchical trans-dimensional Bayesian inversion of fundamental mode of SPAC data that properly accounts for the correlated data noise, samples the V P / V s ratio and relaxes the number of layers (i.e. model parametrization) to be unknown in the inversion. We further examine the limitation of using only fundamental modes in the inversion. Our synthetic experiments show that the inversion recovers an incorrect model unless we sample the correlated noise and V P / V s ratio in the inversion. The inversion is then applied to SPAC data acquired at 19 sites across the Tacoma basin in Washington State to characterize the V s and the time-averaged V s over 30-m depth ( V s 30 ). Our results show that the V s 30 varies from ∼200 to 800 m s −1 . The V s 30 within the basin is higher in the middle and lower on the east and west sides. We find that these V s 30 values vary with geologic unit. The uncertainties for V s 30 are within 20 m s −1 in average except for the most eastern site TB28. Additionally, the uncertainties are greater for deeper depths beneath most of the sites as the sensitivity decreases as a function of depth. The Vs structure as a function of depth is also complex beneath some sites, possibly because the SPAC curves are affected by higher order Rayleigh modes that are not considered in the inversion. To better constrain the deeper V s structure, V s 30 and/or other average measures of V s over depth, additional constraints from complementary data, such as ellipticity or geologic data are needed. Moreover, our synthetic experiments show that higher order modes can have significant effect in the inversion results, particularly when there is a low-velocity layer.

Washington↗
USGS2016

Uncertainty in biological monitoring: a framework for data collection and analysis to account for multiple sources of sampling bias

Biological monitoring programmes are increasingly relying upon large volumes of citizen-science data to improve the scope and spatial coverage of information, challenging the scientific community to develop design and model-based approaches to improve inference. Recent statistical models in ecology have been developed to accommodate false-negative errors, although current work points to false-positive errors as equally important sources of bias. This is of particular concern for the success of any monitoring programme given that rates as small as 3% could lead to the overestimation of the occurrence of rare events by as much as 50%, and even small false-positive rates can severely bias estimates of occurrence dynamics. We present an integrated, computationally efficient Bayesian hierarchical model to correct for false-positive and false-negative errors in detection/non-detection data. Our model combines independent, auxiliary data sources with field observations to improve the estimation of false-positive rates, when a subset of field observations cannot be validated a posteriori or assumed as perfect. We evaluated the performance of the model across a range of occurrence rates, false-positive and false-negative errors, and quantity of auxiliary data. The model performed well under all simulated scenarios, and we were able to identify critical auxiliary data characteristics which resulted in improved inference. We applied our false-positive model to a large-scale, citizen-science monitoring programme for anurans in the north-eastern United States, using auxiliary data from an experiment designed to estimate false-positive error rates. Not correcting for false-positive rates resulted in biased estimates of occupancy in 4 of the 10 anuran species we analysed, leading to an overestimation of the average number of occupied survey routes by as much as 70%. The framework we present for data collection and analysis is able to efficiently provide reliable inference for occurrence patterns using data from a citizen-science monitoring programme. However, our approach is applicable to data generated by any type of research and monitoring programme, independent of skill level or scale, when effort is placed on obtaining auxiliary information on false-positive rates.

Methods in Ecology and Evolution↗
USGS2019

Occupancy models for citizen-science data

Large‐scale citizen‐science projects, such as atlases of species distribution, are an important source of data for macroecological research, for understanding the effects of climate change and other drivers on biodiversity, and for more applied conservation tasks, such as early‐warning systems for biodiversity loss. However, citizen‐science data are challenging to analyse because the observation process has to be taken into account. Typically, the observation process leads to heterogeneous and non‐random sampling, false absences, false detections, and spatial correlations in the data. Increasingly, occupancy models are being used to analyse atlas data. We advocate a dual approach to strengthen inference from citizen science data for the questions the programme is intended to address: (a) the survey design should be chosen with a particular set of questions and associated analysis strategy in mind and (b) the statistical methods should be tailored not only to those questions but also to the specific characteristics of the data. We review the consequences of particular survey design choices that typically need to be made in atlas‐style citizen‐science projects. These include spatial resolution of the sampling units, allocation of effort in space, and collection of information about the observation process. On the analysis side, we review extensions of the basic occupancy models that are frequently necessary with atlas data, including methods for dealing with heterogeneity, non‐independent detections, false detections, and violation of the closure assumption. New technologies, such as cell‐phone apps and fixed remote detection devices, are revolutionizing citizen‐science projects. There is an opportunity to maximize the usefulness of the resulting datasets if the protocols are rooted in robust statistical designs and data analysis issues are being considered. Our review provides guidelines for designing new projects and an overview of the current methods that can be used to analyse data from such projects.

Methods in Ecology and Evolution↗
USGS2006

A genetic algorithm to reduce stream channel cross section data

A genetic algorithm (GA) was used to reduce cross section data for a hypothetical example consisting of 41 data points and for 10 cross sections on the Kootenai River. The number of data points for the Kootenai River cross sections ranged from about 500 to more than 2,500. The GA was applied to reduce the number of data points to a manageable dataset because most models and other software require fewer than 100 data points for management, manipulation, and analysis. Results indicated that the program successfully reduced the data. Fitness values from the genetic algorithm were lower (better) than those in a previous study that used standard procedures of reducing the cross section data. On average, fitnesses were 29 percent lower, and several were about 50 percent lower. Results also showed that cross sections produced by the genetic algorithm were representative of the original section and that near-optimal results could be obtained in a single run, even for large problems. Other data also can be reduced in a method similar to that for cross section data.

Journal of the American Water Resources Associatio↗
USGS2013

An effective noise-suppression technique for surface microseismic data

The presence of strong surface-wave noise in surface microseismic data may decrease the utility of these data. We implement a technique, based on the distinct characteristics that microseismic signal and noise show in the τ‐p domain, to suppress surface-wave noise in microseismic data. Because most microseismic source mechanisms are deviatoric, preprocessing is necessary to correct for the nonuniform radiation pattern prior to transforming the data to the τ‐p domain. We employ a scanning approach, similar to semblance analysis, to test all possible double-couple orientations to determine an estimated orientation that best accounts for the polarity pattern of any microseismic events. We then correct the polarity of the data traces according to this pattern, prior to conducting signal-noise separation in the τ‐p domain. We apply our noise-suppression technique to two surface passive-seismic data sets from different acquisition surveys. The first data set includes a synthetic microseismic event added to field passive noise recorded by an areal receiver array distributed over a Barnett Formation reservoir undergoing hydraulic fracturing. The second data set is field microseismic data recorded by receivers arranged in a star-shaped array, over a Bakken Shale reservoir during a hydraulic-fracturing process. Our technique significantly improves the signal-to-noise ratios of the microseismic events and preserves the waveforms at the individual traces. We illustrate that the enhancement in signal-to-noise ratio also results in improved imaging of the microseismic hypocenter.

Geophysics↗
USGS2014

Multielevation calibration of frequency-domain electromagnetic data

Systematic calibration errors must be taken into account because they can substantially impact the accuracy of inverted subsurface resistivity models derived from frequency-domain electromagnetic data, resulting in potentially misleading interpretations. We have developed an approach that uses data acquired at multiple elevations over the same location to assess calibration errors. A significant advantage is that this method does not require prior knowledge of subsurface properties from borehole or ground geophysical data (though these can be readily incorporated if available), and is, therefore, well suited to remote areas. The multielevation data were used to solve for calibration parameters and a single subsurface resistivity model that are self consistent over all elevations. The deterministic and Bayesian formulations of the multielevation approach illustrate parameter sensitivity and uncertainty using synthetic- and field-data examples. Multiplicative calibration errors (gain and phase) were found to be better resolved at high frequencies and when data were acquired over a relatively conductive area, whereas additive errors (bias) were reasonably resolved over conductive and resistive areas at all frequencies. The Bayesian approach outperformed the deterministic approach when estimating calibration parameters using multielevation data at a single location; however, joint analysis of multielevation data at multiple locations using the deterministic algorithm yielded the most accurate estimates of calibration parameters. Inversion results using calibration-corrected data revealed marked improvement in misfit, lending added confidence to the interpretation of these models.

Geophysics↗
USGS2006

Cumulative uncertainty in measured streamflow and water quality data for small watersheds

The scientific community has not established an adequate understanding of the uncertainty inherent in measured water quality data, which is introduced by four procedural categories: streamflow measurement, sample collection, sample preservation/storage, and laboratory analysis. Although previous research has produced valuable information on relative differences in procedures within these categories, little information is available that compares the procedural categories or presents the cumulative uncertainty in resulting water quality data. As a result, quality control emphasis is often misdirected, and data uncertainty is typically either ignored or accounted for with an arbitrary margin of safety. Faced with the need for scientifically defensible estimates of data uncertainty to support water resource management, the objectives of this research were to: (1) compile selected published information on uncertainty related to measured streamflow and water quality data for small watersheds, (2) use a root mean square error propagation method to compare the uncertainty introduced by each procedural category, and (3) use the error propagation method to determine the cumulative probable uncertainty in measured streamflow, sediment, and nutrient data. Best case, typical, and worst case data quality scenarios were examined. Averaged across all constituents, the calculated cumulative probable uncertainty (%) contributed under typical scenarios ranged from 6% to 19% for streamflow measurement, from 4% to 48% for sample collection, from 2% to 16% for sample preservation/storage, and from 5% to 21% for laboratory analysis. Under typical conditions, errors in storm loads ranged from 8% to 104% for dissolved nutrients, from 8% to 110% for total N and P, and from 7% to 53% for TSS. Results indicated that uncertainty can increase substantially under poor measurement conditions and limited quality control effort. This research provides introductory scientific estimates of uncertainty in measured water quality data. The results and procedures presented should also assist modelers in quantifying the quality of calibration and evaluation data sets, determining model accuracy goals, and evaluating model performance.

Transactions of the ASABE↗
USGS1999

Annual survival of Snail Kites in Florida: Radio telemetry versus capture-resighting data

We estimated annual survival of Snail Kites ( Rostrhamus sociabilis ) in Florida using the Kaplan-Meier estimator with data from 271 radio-tagged birds over a three-year period and capture-recapture (resighting) models with data from 1,319 banded birds over a six-year period. We tested the hypothesis that survival differed among three age classes using both data sources. We tested additional hypotheses about spatial and temporal variation using a combination of data from radio telemetry and single- and multistrata capture-recapture models. Results from these data sets were similar in their indications of the sources of variation in survival, but they differed in some parameter estimates. Both data sources indicated that survival was higher for adults than for juveniles, but they did not support delineation of a subadult age class. Our data also indicated that survival differed among years and regions for juveniles but not for adults. Estimates of juvenile survival using radio telemetry data were higher than estimates using capture-recapture models for two of three years (1992 and 1993). Ancillary evidence based on censored birds indicated that some mortality of radio-tagged juveniles went undetected during those years, resulting in biased estimates. Thus, we have greater confidence in our estimates of juvenile survival using capture-recapture models. Precision of estimates reflected the number of parameters estimated and was surprisingly similar between radio telemetry and single-stratum capture-recapture models, given the substantial differences in sample sizes. Not having to estimate resighting probability likely offsets, to some degree, the smaller sample sizes from our radio telemetry data. Precision of capture-recapture models was lower using multistrata models where region-specific parameters were estimated than using single-stratum models, where spatial variation in parameters was not taken into account.

The Auk↗