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Results for “Journal of Applied Statistics”

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At least 37 records · Page 2Linked to original sources
USGS1999

Controlling for varying effort in count surveys: An analysis of Christmas Bird Count data

The Christmas Bird Count (CBC) is a valuable source of information about midwinter populations of birds in the continental U.S. and Canada. Analysis of CBC data is complicated by substantial variation among sites and years in effort expended in counting; this feature of the CBC is common to many other wildlife surveys. Specification of a method for adjusting counts for effort is a matter of some controversy. Here, we present models for longitudinal count surveys with varying effort; these describe the effect of effort as proportional to exp(B effortp), where B and p are parameters. For any fixed p, our models are loglinear in the transformed explanatory variable (effort)p and other covariables. Hence, we fit a collection of loglinear models corresponding to a range of values of p and select the best effort adjustment from among these on the basis of fit statistics. We apply this procedure to data for six bird species in five regions, for the period 1959-1988.

Journal of Agricultural, Biological, and Environme↗
USGS2023

Reanalysis indicates little evidence of reduction in eagle mortality rate by automated curtailment of wind turbines

Unintended consequences of renewable energy development include collision-caused deaths of birds and bats. Energy companies may risk prosecution if protected species are among the casualties. Shutting down turbines during high collision-risk conditions could reduce mortality rates, and several companies are developing systems to identify such conditions. A recent peer-reviewed article published in the Journal of Applied Ecology reported a remarkable ‘82% (75%–89%) reduction in the fatality rate’ of eagles at a wind energy facility due to a device marketed as Identiflight®—remarkable because of the impressive effect size and the extremely high level of precision. We show that reported results stem from four major errors, which, when corrected, give an unremarkable estimate of 50% (−159%, 89%) reduction (or possible increase) in the fatality rate. The errors include the following: (i) Ignoring annual variation . They compare the average number of eagle fatalities over 4 years before activation of Identiflight® to the number in a single year after, ignoring annual variation in fatalities. (ii) Unfounded causal inference . Lack of replication (one treatment year at one site) is ignored, leading to unwarranted causal inference. (iii) Inflated effect size . Effect size is inflated by assuming (without providing evidence) that the difference in fatality relative to the mean at a neighbouring site would be exactly repeated at the treatment site. Furthermore, the observed difference in fatalities at the control site depends strongly on the arbitrarily chosen date distinguishing the ‘Before’ and ‘After’ periods, yielding unreliable results. (iv) Inconsistency of data . It is unclear why 7 of 42 reported eagle fatalities were not included in the data analyzed, potentially further inflating the estimated effect size. Synthesis and applications . The recent claim, published in the Journal of Applied Ecology, that ‘Eagle fatalities are reduced by automated curtailment of wind turbines’ is not supported by the data but stems from errors that led to strongly overstated effect size and precision, and unfounded inference. In theory, automated curtailment has obvious potential for reducing eagle fatalities, but several more years of data at several locations and appropriate statistical analyses will be required to evaluate its effectiveness and to inform management prescriptions involving this technology.

Journal of Applied Ecology↗
USGS1959

Probability analysis applied to a water-supply problem

The literature on probability techniques applicable to problems in hydrology is abundant but scattered through scientific journals of both hydrology and statistics. Important administrative and judicial decisions presently face water-compact commissions, courts, and water-planning committees. These and other groups might find useful, a brief and simplified discussion of how statistical techniques can aid in analysing problems of water supply. The interest expressed in this subject by various parties to the litigation concerning the Colorado River prompts this publication of material, which was presented in August 1958 before the Special Master of the Supreme Court hearing the proceedings of Arizona v. California et al . The examples presented here are the same as those used in testimony before the Special Master, but there are included the basic computations, which were too detailed to present in the actual testimony. The specific example, which was analyzed in that testimony, was a 61-year series of annual discharge values of the Colorado River at Lees Ferry, 1896 to 1956, inclusive. However, the methodology presented herein is generally applicable to many other streamflow records; and the specific data discussed should be viewed as exemplifying the types of information, which can be obtained from any streamflow record.

Circular↗
USGS2011

Mapping rice areas of South Asia using MODIS multitemporal data

Our goal is to map the rice areas of six South Asian countries using moderate-resolution imaging spectroradiometer (MODIS) time-series data for the time period 2000 to 2001. South Asia accounts for almost 40% of the world's harvested rice area and is also home to 74% of the population that lives on less than $2.00 a day. The population of the region is growing faster than its ability to produce rice. Thus, accurate and timely assessment of where and how rice is cultivated is important to craft food security and poverty alleviation strategies. We used a time series of eight-day, 500-m spatial resolution composite images from the MODIS sensor to produce rice maps and rice characteristics (e.g., intensity of cropping, cropping calendar) taking data for the years 2000 to 2001 and by adopting a suite of methods that include spectral matching techniques, decision trees, and ideal temporal profile data banks to rapidly identify and classify rice areas over large spatial extents. These methods are used in conjunction with ancillary spatial data sets (e.g., elevation, precipitation), national statistics, and maps, and a large volume of field-plot data. The resulting rice maps and statistics are compared against a subset of independent field-plot points and the best available subnational statistics on rice areas for the main crop growing season (kharif season). A fuzzy classification accuracy assessment for the 2000 to 2001 rice-map product, based on field-plot data, demonstrated accuracies from 67% to 100% for individual rice classes, with an overall accuracy of 80% for all classes. Most of the mixing was within rice classes. The derived physical rice area was highly correlated with the subnational statistics with R 2 values of 97% at the district level and 99% at the state level for 2000 to 2001. These results suggest that the methods, approaches, algorithms, and data sets we used are ideal for rapid, accurate, and large-scale mapping of paddy rice as well as for generating their statistics over large areas.

South Asia↗
USGS2004

N-mixture models for estimating population size from spatially replicated counts

Spatial replication is a common theme in count surveys of animals. Such surveys often generate sparse count data from which it is difficult to estimate population size while formally accounting for detection probability. In this article, I describe a class of models (n-mixture models) which allow for estimation of population size from such data. The key idea is to view site-specific population sizes, n, as independent random variables distributed according to some mixing distribution (e.g., Poisson). Prior parameters are estimated from the marginal likelihood of the data, having integrated over the prior distribution for n. Carroll and Lombard (1985, Journal of American Statistical Association 80, 423-426) proposed a class of estimators based on mixing over a prior distribution for detection probability. Their estimator can be applied in limited settings, but is sensitive to prior parameter values that are fixed a priori. Spatial replication provides additional information regarding the parameters of the prior distribution on n that is exploited by the n-mixture models and which leads to reasonable estimates of abundance from sparse data. A simulation study demonstrates superior operating characteristics (bias, confidence interval coverage) of the n-mixture estimator compared to the Caroll and Lombard estimator. Both estimators are applied to point count data on six species of birds illustrating the sensitivity to choice of prior on p and substantially different estimates of abundance as a consequence.

Biometrics↗
USGS1995

Estimation and confidence intervals for empirical mixing distributions

Questions regarding collections of parameter estimates can frequently be expressed in terms of an empirical mixing distribution (EMD). This report discusses empirical Bayes estimation of an EMD, with emphasis on the construction of interval estimates. Estimation of the EMD is accomplished by substitution of estimates of prior parameters in the posterior mean of the EMD. This procedure is examined in a parametric model (the normal-normal mixture) and in a semi-parametric model. In both cases, the empirical Bayes bootstrap of Laird and Louis (1987, Journal of the American Statistical Association 82, 739-757) is used to assess the variability of the estimated EMD arising from the estimation of prior parameters. The proposed methods are applied to a meta-analysis of population trend estimates for groups of birds.

Biometrics↗
USGS2013

Computationally efficient statistical differential equation modeling using homogenization

Statistical models using partial differential equations (PDEs) to describe dynamically evolving natural systems are appearing in the scientific literature with some regularity in recent years. Often such studies seek to characterize the dynamics of temporal or spatio-temporal phenomena such as invasive species, consumer-resource interactions, community evolution, and resource selection. Specifically, in the spatial setting, data are often available at varying spatial and temporal scales. Additionally, the necessary numerical integration of a PDE may be computationally infeasible over the spatial support of interest. We present an approach to impose computationally advantageous changes of support in statistical implementations of PDE models and demonstrate its utility through simulation using a form of PDE known as “ecological diffusion.” We also apply a statistical ecological diffusion model to a data set involving the spread of mountain pine beetle (Dendroctonus ponderosae) in Idaho, USA.

Journal of Agricultural, Biological, and Environme↗
USGS2016

Climate adaption and post-fire restoration of a foundational perennial in cold desert: Insights from intraspecific variation in response to weather

1.The loss of foundational but fire-intolerant perennials such as sagebrush due to increases in fire size and frequency in semiarid regions has motivated efforts to restore them, often with mixed or even no success. Seeds of sagebrush Artemisia tridentata and related species must be moved considerable distances from seed source to planting sites, but such transfers have not been guided by an understanding of local climate adaptation. Initial seedling establishment and its response to weather are a key demographic bottleneck that likely varies among subspecies and populations of sagebrush. 2.We assessed differences in survival, growth, and physiological responses of sagebrush to weather among eleven seed sources that varied in subspecies, cytotype, and climates-of-origin over 18 months following outplanting. Diploid or polyploid populations of mountain, Wyoming, and basin big sagebrush ( A.tridentata ssp. vaseyana, A.tridentata ssp. wyomingensis, and A.tridentata ssp. tridentata , respectively) were planted onto five burned sites that normally support A.t.wyomingensis with some A.t.tridentata . 3.A.t.wyomingensis had the most growth and survival, and tetraploid populations had greater survival and height than diploids. Seasonal timing of mortality varied among the subspecies/cytotypes and was more closely related to minimum temperatures than water deficit. 4.Temperatures required to induce ice formation were up to 6°C more negative in 4n- A.t.tridentata and A.t.wyomingensis than other subspecies/cytotypes, indicating greater freezing avoidance. In contrast, freezing resistance of photosynthesis varied only 1°C among subspecies/cytotypes, being greatest in A.t.wyomingensis and least in the subspecies normally considered most cold-adapted, A.t.vaseyana . A large spectrum of reliance on freezing-avoidance vs. freezing-tolerance was observed and corresponded to differences in post-fire survivorship among subspecies/cytotypes. Differences in water deficit responses among subspecies/cytotypes were not as strong and did not relate to survival patterns. 5. Synthesis and applications. Low temperature responses are a key axis defining climate adaptation in young sagebrush seedlings and vary more with cytotype than with subspecies, which contrasts with the traditional emphases on (i) water limitations to explain establishment in these deserts, and (ii) subspecies in selecting restoration seedings. These important and novel insights on climate adaptation are critical for seed selection and parameterizing seed transfer zones, and were made possible by incorporating weather data with survival statistics. The survival/weather statistics used here could be applied to any restoration planting or seeding to help elucidate factors contributing to success and enable adaptive management.

Journal of Applied Ecology↗
USGS1986

Recalibration and predictive reliability of a solute-transport model of an irrigated stream-aquifer system

A solute-transport model of an irrigated stream-aquifer system was recalibrated because of discrepancies between prior predictions of ground-water salinity trends during 1971-1982 and the observed outcome in February 1982. The original model was calibrated with a 1-year record of data collected during 1971-1972 in an 18-km reach of the Arkansas River Valley in southeastern Colorado. The model is improved by incorporating additional hydrologic processes (salt transport through the unsaturated zone) and through reexamination of the reliability of some input data (regression relationship used to estimate salinity from specific conductance data). Extended simulations using the recalibrated model are made to investigate the usefulness of the model for predicting long-term trends of salinity and water levels within the study area. Predicted ground-water levels during 1971-1982 are in good agreement with the observed, indicating that the original 1971-1972 study period was sufficient to calibrate the flow model. However, long-term simulations using the recalibrated model based on recycling the 1971-1972 data alone yield an average ground-water salinity for 1982 that is too low by about 10%. Simulations that incorporate observed surface-water salinity variations yield better results, in that the calculated average ground-water salinity for 1982 is within 3% of the observed value. Statistical analysis of temporal salinity variations of the applied surface water indicates that at least a 4-year sampling period is needed to accurately calibrate the transport model.

Colorado↗
USGS2011

Has the magnitude of floods across the USA changed with global CO2 levels?

Statistical relationships between annual floods at 200 long-term (85–127 years of record) streamgauges in the coterminous United States and the global mean carbon dioxide concentration (GMCO2) record are explored. The streamgauge locations are limited to those with little or no regulation or urban development. The coterminous US is divided into four large regions and stationary bootstrapping is used to evaluate if the patterns of these statistical associations are significantly different from what would be expected under the null hypothesis that flood magnitudes are independent of GMCO2. In none of the four regions defined in this study is there strong statistical evidence for flood magnitudes increasing with increasing GMCO2. One region, the southwest, showed a statistically significant negative relationship between GMCO2 and flood magnitudes. The statistical methods applied compensate both for the inter-site correlation of flood magnitudes and the shorter-term (up to a few decades) serial correlation of floods.

Hydrological Sciences Journal↗
USGS2002

Crosswell seismic investigation of hydraulically conductive, fracture bedrock near Mirror Lake, New Hampshire

Near Mirror Lake, New Hampshire (USA), hydraulically conductive, fractured bedrock was investigated with the crosswell seismic method to determine whether this method could provide any information about hydraulic conductivity between wells. To this end, crosswell seismic data, acoustic logs from boreholes, image logs from boreholes, and single borehole hydraulic tests were analyzed. The analysis showed that, first, the P-wave velocities from the acoustic logs tended to be higher in schist than they were in granite. (Schist and granite were the dominant rock types). Second, the P-wave velocities from the acoustic logs tended to be low near fractures. Third, the hydraulic conductivity was always low (always less than to 10 −8 m/s) where no fractures intersected the borehole, but the hydraulic conductivity ranged from low to high (from less than to 10 −10 m/s to 10 −4 m/s) where one or more fractures intersected the borehole. Fourth, high hydraulic conductivities were slightly more frequent when the P-wave velocity was low (less than 5200 m/s) than when it was high (greater than or equal to 5200 m/s). The interpretation of this statistical relation was that the fractures tended to increase the hydraulic conductivity and to lower the P-wave velocity. This statistical relation was applied to a velocity tomogram to create a map showing the probability of high hydraulic conductivity; the map was consistent with results from independent hydraulic tests.

New Hampshire↗
USGS2017

A multimodal wave spectrum-based approach for statistical downscaling of local wave climate

Characterization of wave climate by bulk wave parameters is insufficient for many coastal studies, including those focused on assessing coastal hazards and long-term wave climate influences on coastal evolution. This issue is particularly relevant for studies using statistical downscaling of atmospheric fields to local wave conditions, which are often multimodal in large ocean basins (e.g. the Pacific). Swell may be generated in vastly different wave generation regions, yielding complex wave spectra that are inadequately represented by a single set of bulk wave parameters. Furthermore, the relationship between atmospheric systems and local wave conditions is complicated by variations in arrival time of wave groups from different parts of the basin. Here, we address these two challenges by improving upon the spatiotemporal definition of the atmospheric predictor used in statistical downscaling of local wave climate. The improved methodology separates the local wave spectrum into “wave families,” defined by spectral peaks and discrete generation regions, and relates atmospheric conditions in distant regions of the ocean basin to local wave conditions by incorporating travel times computed from effective energy flux across the ocean basin. When applied to locations with multimodal wave spectra, including Southern California and Trujillo, Peru, the new methodology improves the ability of the statistical model to project significant wave height, peak period, and direction for each wave family, retaining more information from the full wave spectrum. This work is the base of statistical downscaling by weather types, which has recently been applied to coastal flooding and morphodynamic applications.

Journal of Physical Oceanography↗
USGS2017

Imputation approaches for animal movement modeling

The analysis of telemetry data is common in animal ecological studies. While the collection of telemetry data for individual animals has improved dramatically, the methods to properly account for inherent uncertainties (e.g., measurement error, dependence, barriers to movement) have lagged behind. Still, many new statistical approaches have been developed to infer unknown quantities affecting animal movement or predict movement based on telemetry data. Hierarchical statistical models are useful to account for some of the aforementioned uncertainties, as well as provide population-level inference, but they often come with an increased computational burden. For certain types of statistical models, it is straightforward to provide inference if the latent true animal trajectory is known, but challenging otherwise. In these cases, approaches related to multiple imputation have been employed to account for the uncertainty associated with our knowledge of the latent trajectory. Despite the increasing use of imputation approaches for modeling animal movement, the general sensitivity and accuracy of these methods have not been explored in detail. We provide an introduction to animal movement modeling and describe how imputation approaches may be helpful for certain types of models. We also assess the performance of imputation approaches in two simulation studies. Our simulation studies suggests that inference for model parameters directly related to the location of an individual may be more accurate than inference for parameters associated with higher-order processes such as velocity or acceleration. Finally, we apply these methods to analyze a telemetry data set involving northern fur seals ( Callorhinus ursinus ) in the Bering Sea. Supplementary materials accompanying this paper appear online.

Journal of Agricultural, Biological, and Environme↗
USGS2019

Balancing sampling intensity against spatial coverage for a community science monitoring programme

Community science is an increasingly integral part of biodiversity research and monitoring, often achieving broad spatial and temporal coverage but lower sampling intensity than studies conducted by professional scientists. When designing a community‐science monitoring programme, careful assessment of sampling designs that could be both feasible and successful at meeting programme goals is essential. Monarch butterflies ( Danaus plexippus ) are the focus of several successful community‐science projects in the U.S., but broader coverage is needed to monitor breeding areas and explain population declines observed in overwintering areas. The U.S. Monarch Conservation Science Partnership's Integrated Monarch Monitoring Program (IMMP) will representatively monitor monarchs and milkweed across North America. We performed a simulation‐based power analysis to predict trade‐offs between sampling breadth (number of sites and years) and sampling intensity (number of visits or subplots per site and year) for the IMMP. We evaluated whether each sampling design would produce sufficient statistical power to detect population trends and differences among land‐use sectors in densities of milkweed, monarch eggs, and adult monarchs. Sampling breadth had much stronger effects than sampling intensity on statistical power for all three monitoring targets. Depending on land‐use sector, monitoring 400–800 sites over 10–15 years would detect trends in densities of milkweed and adult monarchs, but no scenarios were successful for monarch eggs. Sampling 400–800 sites would also detect small (for adult monarchs) to large (for milkweed) differences among land‐use sectors in density of all three monitoring targets within the first 2–5 years. As more data become available from the IMMP, the sampling goals can be updated. Synthesis and applications . Careful sample design is an essential step in developing a successful monitoring programme. For monarchs and milkweed, we found that sampling breadth (number of sites and years) had a much stronger effect on statistical power than sampling intensity (number of visits or subsamples per site), suggesting field protocols could be tailored to maximize recruitment and retention of volunteers by minimizing the effort required to monitor each site. Many long‐term monitoring programmes might similarly benefit from evaluating the statistical trade‐offs between sampling breadth and intensity in their sampling designs.

Journal of Applied Ecology↗
USGS2009

A cautionary note on substituting spatial subunits for repeated temporal sampling in studies of site occupancy

1. Assessing the probability that a given site is occupied by a species of interest is important to resource managers, as well as metapopulation or landscape ecologists. Managers require accurate estimates of the state of the system, in order to make informed decisions. Models that yield estimates of occupancy, while accounting for imperfect detection, have proven useful by removing a potentially important source of bias. To account for detection probability, multiple independent searches per site for the species are required, under the assumption that the species is available for detection during each search of an occupied site. 2. We demonstrate that when multiple samples per site are defined by searching different locations within a site, absence of the species from a subset of these spatial subunits induces estimation bias when locations are exhaustively assessed or sampled without replacement. 3. We further demonstrate that this bias can be removed by choosing sampling locations with replacement, or if the species is highly mobile over a short period of time. 4. Resampling an existing data set does not mitigate bias due to exhaustive assessment of locations or sampling without replacement. 5. Synthesis and applications. Selecting sampling locations for presence/absence surveys with replacement is practical in most cases. Such an adjustment to field methods will prevent one source of bias, and therefore produce more robust statistical inferences about species occupancy. This will in turn permit managers to make resource decisions based on better knowledge of the state of the system.

Journal of Applied Ecology↗
USGS2021

Assessment of a claimed ultra-low frequency electromagnetic (ULFEM) earthquake precursor

The rate of occurrence of anomalous ultra-low frequency electromagnetic (ULFEM) pulses has been claimed to have increased days to weeks prior to the M5.4 2007 and M4.0 2010 Alum Rock earthquakes. We re-examine the previously reported ultra-low frequency (ULF: 0.01–10 Hz) magnetic data recorded at a QuakeFinder site located 9 km from the earthquake hypocentre, and compare to data from a nearby Stanford-USGS site located 42 km from the hypocentre, to analyse the characteristics of the pulses and assess their origin. Using pulse definitions and pulse-counting algorithms analogous to those previously reported, we corroborate the increase in pulse counts before the 2007 Alum Rock earthquake at the QuakeFinder station, but we note that the number of pulses depends on chosen temporal and amplitude detection thresholds. These thresholds are arbitrary because we lack a clear physical model or basis for their selection. We do not see the same increase in pulse counts before the 2010 Alum Rock earthquake at the QuakeFinder or Stanford-USGS stations. In addition, the majority of pulses in the QuakeFinder data and Stanford-USGS data do not match temporally, indicating the pulses lack a common origin and are not from lightning or solar-driven ionospheric/magnetospheric disturbances. Our assessment of the temporal distribution of pulse counts shows pulse counts increase during peak human activity hours, suggesting these pulses result from local cultural noise and are not tectonic in origin. The many unknowns about the character and even existence of precursory earthquake pulses means that standard numerical and statistical tests cannot easily be applied. Yet here we show that exhaustive investigation of many different aspects of ULFEM signals can be used to properly characterize their origin.

California↗
USGS1988

A field test for differences in condition among trapped and shot mallards

We tested predictions from the condition bias hypothesis (Weatherland and Greenwood 1981) regarding the effects of sampling methods of body weights of mallards ( Anas platyrhynchos ) at White River National Wildlife Refuge (WRNWR), Arkansas, during 24 November-8 December 1985. Body weights of 84 mallards caught with unbaited rocket nets in a natural wetland were used as experimental controls and compared to the body weights of 70 mallards captured with baited rocket nets, 86 mallards captured with baited swim-in traps, and 130 mallards killed by hunters. We found no differences (P > 0.27) in body weight among sampling methods, but body condition (wt/wing length) of the birds killed by hunters was less (P < 0.02) than that of the controls. Power of the test statistics to reject the null hypothesis of equal body weights among sampling methods was > 0.75 for differences > 50 g. The condition bias hypothesis probably applies to ducks killed by hunters but not to trapping operations when substantial (> 20 at 1 time) numbers of birds are captured.

Arkansas↗
USGS1978

Low-cost computer classification of land cover in the Portland area, Oregon, by signature extension techniques

Computer-aided techniques for interpreting multispectral data acquired by Landsat offer economies in the mapping of land cover. Even so, the actual establishment of the statistical classes, or "signatures," is one of the relatively more costly operations involved. Analysts have, therefore, been seeking cost-saving signature extension techniques that would accept training data acquired at one time or place and apply them to another. Signatures may be extended in preprocessing steps or in the classification steps that follow. In the present example, land cover classes were derived by the simplest and most direct form of signature extension: Classes statistically derived from a Landsat scene of the Puget Sound area, Wash., were applied to the adjacent Landsat scene of the Portland area, Oreg., acquired during the next 25 seconds down orbit. Many features can be recognized on the reduced-scale version of the Portland land cover map shown in this report although no statistical assessment of its accuracy is available.

Oregon, Washington↗