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At least 91 records · Page 5Linked to original sources
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↗
USGS2024

An integrative paradigm for building causal knowledge

A core aspiration of the ecological sciences is to determine how systems work, which implies the challenge of developing a causal understanding. Causal inference has long been approached from a statistical perspective, which can be limited and restrictive for a variety of reasons. Ecologists and other natural scientists have historically pursued mechanistic knowledge as an alternative approach to causal understanding, though without explicit reference to the requirements of causal statistics. In this paper, I describe the premises of an expanded paradigm for causal studies, the Integrative Causal Investigation Paradigm, that subsumes causal statistics and mechanistic investigation into a multi-evidence approach. This paradigm is distinct from the one articulated by causal statistics in that it (1) focuses its attention on the long-term goal of building causal knowledge across multiple studies and (2) recognizes the essential role of mechanistic investigations in establishing a causal understanding. The Integrative Paradigm, consequentially, proposes that there are multiple methodological routes to building causal knowledge and thus represents a pluralistic perspective. This paper begins by describing the crux of the problem faced by causal statistics. To understand this problem, it should be recognized that the word causal has multiple meanings and a variety of evidential standards. An expanded vocabulary is developed so as to reduce ambiguities and clarify critical issues. I further show by example that there is an important ingredient typically omitted from consideration in causal statistics, which is the known information related to the mechanisms underlying relationships being evaluated. To address this issue, I describe a procedure, Causal Knowledge Analysis, that involves an evaluation and compilation of existing evidence indicative of causal content and the features of mechanisms. Causal Knowledge Analysis is applied to three example situations to illustrate the process and its potential for contributing to the development of causal knowledge. The implications of adopting the proposed paradigm and associated procedures are discussed and include the potential for advancing ecology, the potential for clarifying causal methodology, and the potential for contributing to predictive forecasting.

Ecological Monographs↗
USGS2016

Structure of high latitude currents in global magnetospheric-ionospheric models

Using three resolutions of the Lyon-Fedder-Mobarry global magnetosphere-ionosphere model (LFM) and the Weimer 2005 empirical model we examine the structure of the high latitude field-aligned current patterns. Each resolution was run for the entire Whole Heliosphere Interval which contained two high speed solar wind streams and modest interplanetary magnetic field strengths. Average states of the field-aligned current (FAC) patterns for 8 interplanetary magnetic field clock angle directions are computed using data from these runs. Generally speaking the patterns obtained agree well with results obtained from the Weimer 2005 computing using the solar wind and IMF conditions that correspond to each bin. As the simulation resolution increases the currents become more intense and narrow. A machine learning analysis of the FAC patterns shows that the ratio of Region 1 (R1) to Region 2 (R2) currents decreases as the simulation resolution increases. This brings the simulation results into better agreement with observational predictions and the Weimer 2005 model results. The increase in R2 current strengths also results in the cross polar cap potential (CPCP) pattern being concentrated in higher latitudes. Current-voltage relationships between the R1 and CPCP are quite similar at the higher resolution indicating the simulation is converging on a common solution. We conclude that LFM simulations are capable of reproducing the statistical features of FAC patterns.

Space Science Reviews↗
USGS2001

Suggestions for presenting the results of data analyses

We give suggestions for the presentation of research results from frequentist, information-theoretic, and Bayesian analysis paradigms, followed by several general suggestions. The information-theoretic and Bayesian methods offer alternative approaches to data analysis and inference compared to traditionally used methods. Guidance is lacking on the presentation of results under these alternative procedures and on nontesting aspects of classical frequentists methods of statistical analysis. Null hypothesis testing has come under intense criticism. We recommend less reporting of the results of statistical tests of null hypotheses in cases where the null is surely false anyway, or where the null hypothesis is of little interest to science or management.

Journal of Wildlife Management↗
USGS2007

Earthquake likelihood model testing

INTRODUCTION The Regional Earthquake Likelihood Models (RELM) project aims to produce and evaluate alternate models of earthquake potential (probability per unit volume, magnitude, and time) for California. Based on differing assumptions, these models are produced to test the validity of their assumptions and to explore which models should be incorporated in seismic hazard and risk evaluation. Tests based on physical and geological criteria are useful but we focus on statistical methods using future earthquake catalog data only. We envision two evaluations: a test of consistency with observed data and a comparison of all pairs of models for relative consistency. Both tests are based on the likelihood method, and both are fully prospective ( i.e. , the models are not adjusted to fit the test data). To be tested, each model must assign a probability to any possible event within a specified region of space, time, and magnitude. For our tests the models must use a common format: earthquake rates in specified “bins” with location, magnitude, time, and focal mechanism limits. Seismology cannot yet deterministically predict individual earthquakes; however, it should seek the best possible models for forecasting earthquake occurrence. This paper describes the statistical rules of an experiment to examine and test earthquake forecasts. The primary purposes of the tests described below are to evaluate physical models for earthquakes, assure that source models used in seismic hazard and risk studies are consistent with earthquake data, and provide quantitative measures by which models can be assigned weights in a consensus model or be judged as suitable for particular regions. In this paper we develop a statistical method for testing earthquake likelihood models. A companion paper ( Schorlemmer and Gerstenberger 2007 , this issue) discusses the actual implementation of these tests in the framework of the RELM initiative. Statistical testing of hypotheses is a common task and a wide range of possible testing procedures exist. Jolliffe and Stephenson ( 2003 ) present different forecast verifications from atmospheric science, among them likelihood testing of probability forecasts and testing the occurrence of binary events. Testing binary events requires that for each forecasted event, the spatial, temporal and magnitude limits be given. Although major earthquakes can be considered binary events, the models within the RELM project express their forecasts on a spatial grid and in 0.1 magnitude units; thus the results are a distribution of rates over space and magnitude. These forecasts can be tested with likelihood tests. In general, likelihood tests assume a valid null hypothesis against which a given hypothesis is tested. The outcome is either a rejection of the null hypothesis in favor of the test hypothesis or a nonrejection, meaning the test hypothesis cannot outperform the null hypothesis at a given significance level. Within RELM, there is no accepted null hypothesis and thus the likelihood test needs to be expanded to allow comparable testing of equipollent hypotheses. To test models against one another, we require that forecasts are expressed in a standard format: the average rate of earthquake occurrence within pre-specified limits of hypocentral latitude, longitude, depth, magnitude, time period, and focal mechanisms. Focal mechanisms should either be described as the inclination of P -axis, declination of P -axis, and inclination of the T -axis, or as strike, dip, and rake angles. Schorlemmer and Gerstenberger ( 2007 , this issue) designed classes of these parameters such that similar models will be tested against each other. These classes make the forecasts comparable between models. Additionally, we are limited to testing only what is precisely defined and consistently reported in earthquake catalogs. Therefore it is currently not possible to test such information as fault rupture length or area, asperity location, etc. Also, to account for data quality issues, we allow for location and magnitude uncertainties as well as the probability that an event is dependent on another event. As we mentioned above, only models with comparable forecasts can be tested against each other. Our current tests are designed to examine grid-based models. This requires that any fault-based model be adapted to a grid before testing is possible. While this is a limitation of the testing, it is an inherent difficulty in any such comparative testing. Please refer to appendix B for a statistical evaluation of the application of the Poisson hypothesis to fault-based models. The testing suite we present consists of three different tests: L-Test, N-Test, and R-Test. These tests are defined similarily to Kagan and Jackson ( 1995 ). The first two tests examine the consistency of the hypotheses with the observations while the last test compares the spatial performances of the models.

Seismological Research Letters↗
USGS2018

Kinematics of the 2015 San Ramon, California earthquake swarm: Implications for fault zone structure and driving mechanisms

Earthquake swarms represent a sudden increase in seismicity that may indicate a heterogeneous fault-zone, the involvement of crustal fluids and/or slow fault slip. Swarms sometimes precede major earthquake ruptures. An earthquake swarm occurred in October 2015 near San Ramon, California in an extensional right step-over region between the northern Calaveras Fault and the Concord–Mt. Diablo fault zone, which has hosted ten major swarms since 1970. The 2015 San Ramon swarm is examined here from 11 October through 18 November using template matching analysis. The relocated seismicity catalog contains ∼4000 events with magnitudes between &#x2212; 0.2 &lt; M d &lt; 3.6 "> −0.2< M d <3.6 . The swarm illuminated three sub-parallel, southwest striking and northwest dipping fault segments of km-scale dimension and thickness of up to 200 m. The segments contain coexisting populations of different focal-mechanisms, suggesting a complex fault zone structure with several sets of en échelon fault orientations. The migration of events along the three planar structures indicates a complex fluid and faulting interaction processes. We searched for correlations between seismic activity and tidal stresses and found some suggestive features, but nothing that we can be confident is statistically significant.

California↗
USGS2007

Assessment of Data for Use in the Development of Nutrient Criteria for Massachusetts Rivers and Streams

The U.S. Geological Survey synthesized, reviewed, and assessed Massachusetts water-quality data for use in the development of either numerical nutrient criteria for rivers and streams or a science-based framework for interpreting narrative criterial for nutrients. Water-quality data collected from 65 Massachusetts locations were selected to represent a wide range, but not a statistical selection, of drainage basins and high-, intermediate-, and low-nutrient ecoregions. Additional sites were selected at some locations to provide data to compare open- and closed-canopy effects on periphyton chlorophyll a concentrations. Nutrient and chlorophyll a concentrations are the primary focus of this study. Data for turbidity, color, dissolved oxygen, specific conductance, pH, and measures of aquatic-plant density also were examined. Water-quality data were analyzed by categories of year, ecoregion, drainage-basin size, Massachusetts nutrient ecoregion, presence of upstream wastewater dischargers, and canopy openness. Graphs and statistical analyses were used to evaluate data. The U.S. Environmental Protection Agency recommends the 25th-percentile value of a water-quality constituent as the numerical nutrient criterion when using all available data for the constituent. In this study of Massachusetts waters, the 25th percentiles of median values at all sampling stations were: total phosphorus, 0.019 milligram per liter (mg/L); total nitrogen, 0.44 (mg/L); and turbidity, 1.2 nephelometric turbidity units (NTU). When the data are sorted by the two USEPA nutrient ecoregions in Massachusetts (VIII and XIV), the new values are: for Ecoregion VIII, total phosphorus, 0.009 (mg/L); total nitrogen, 0.289 (mg/L); and turbidity, 1.7 NTU; for Ecoregion XIV, total phosphorus, 0.028 (mg/L); total nitrogen, 0.583 (mg/L); and turbidity, 3.1 NTU. For the three Massachusetts lake-based nutrient ecoregions, the values are: high-nutrient ecoregion, total phosphorus, 0.030 (mg/L); total nitrogen, 0.642 (mg/L); and turbidity, 1.5 NTU; intermediate-nutrient ecoregion, total phosphorus, 0.016 (mg/L); total nitrogen, 0.419 (mg/L); and turbidity, 1.1 NTU; and low-nutrient ecoregion, total phosphorus, 0.011 (mg/L); total nitrogen, 0.289 (mg/L); and turbidity, 0.7 NTU. In general, median nutrient concentrations were found to be higher in the three following categories of analysis than in their complementary groups: sites in USEPA nutrient Ecoregion XIV, sites downstream from major National Pollutant Discharge Elimination System-permitted wastewater dischargers, and sites in the Massachusetts high-nutrient ecoregion. The largest drainage-basin size class had higher median nitrogen (total and dissolved) concentrations than the smallest, but total median phosphorus concentrations were not significantly different. Median chlorophyll a concentrations did not vary significantly among the categories analyzed. The effects of open and closed canopies on median chlorophyll a concentrations were greater within groups defined by the categories used in this study than between the groups; open-canopy sites generally had higher median chlorophyll a concentrations than closed-canopy sites. More than 40 percent of the sampling stations were located downstream from major wastewater dischargers, and these dischargers were disproportionately located in USEPA Ecoregion XIV and the Massachusetts high-nutrient ecoregion and thus may constitute the same effect on water quality. A number of expected relations among parameters analyzed did not materialize. chlorophyll a did not correlate well with any other parameters. No strong relations among the categories and nutrient concentrations or canopy openness were apparent. The occurrence of antecedent flows exceeding mean annual discharges by 300 percent within 28 days of sample collection did not correlate with decreases in chlorophyll a concentrations that might have resulted from scouring associated with increasing velocities. No rel

Scientific Investigations Report↗
USGS1974

Craters on Earth, Moon, and Mars: Multivariate classification and mode of origin

Testing extraterrestrial craters and candidate terrestrial analogs for morphologic similitude is treated as a problem in numerical taxonomy. According to a principal-components solution and a cluster analysis, 402 representative craters on the Earth, the Moon, and Mars divide into two major classes of contrasting shapes and modes of origin. Craters of net accumulation of material (cratered lunar domes, Martian “calderas,” and all terrestrial volcanoes except maars and tuff rings) group apart from craters of excavation (terrestrial meteorite impact and experimental explosion craters, typical Martian craters, and all other lunar craters). Maars and tuff rings belong to neither group but are transitional. The classification criteria are four independent attributes of topographic geometry derived from seven descriptive variables by the principal-components transformation. Morphometric differences between crater bowl and raised rim constitute the strongest of the four components. Although single topographic variables cannot confidently predict the genesis of individual extraterrestrial craters, multivariate statistical models constructed from several variables can distinguish consistently between large impact craters and volcanoes.

Earth and Planetary Science Letters↗
USGS2020

Informing amphibian conservation efforts with abundance-based metapopulation models

Science-based management strategies are needed to halt or reverse the global decline of amphibians. In many cases, sound management requires reliable models built using monitoring data. Historically, monitoring and statistical modeling efforts have focused on estimating occupancy using detection–nondetection data. Spatial occupancy models are useful for studying colonization–extinction dynamics, but richer insights can be gained from estimating abundance and density-dependent demographic rates. We developed an integrated abundance-based metapopulation model of the processes contributing to spatiotemporal variation in patch population density. We fit our model to a combination of detection–nondetection and count data from a 14-yr study of a reintroduced metapopulation of federally threatened Chiricahua Leopard Frogs (Lithobates chiricahuensis). Pond-specific population growth rate was influenced by pond hydroperiod and frog density, such that permanent and semipermanent ponds with low densities of adult frogs experienced the highest annual population growth rates. Immigration rate declined as the distance among ponds increased. After reintroduction in 2003, metapopulation-level abundance increased and appeared to stabilize around 1300 adult frogs (95% CI = 1192–1471) by year 2015. Further, changes in metapopulation abundance were driven mostly by changes in abundance at a few ponds. These high-density populations, which would not have been identifiable with traditional occupancy-based metapopulation models, are likely especially important for species recovery in the area. Abundance-based metapopulation models can be widely applied to inform conservation efforts, by providing higher quality information needed to prioritize habitat patches for management and can be used to make more accurate predictions of metapopulation extinction risk.

Herpetologica↗
USGS2014

Forecasting distribution of numbers of large fires

Systems to estimate forest fire potential commonly utilize one or more indexes that relate to expected fire behavior; however they indicate neither the chance that a large fire will occur, nor the expected number of large fires. That is, they do not quantify the probabilistic nature of fire danger. In this work we use large fire occurrence information from the Monitoring Trends in Burn Severity project, and satellite and surface observations of fuel conditions in the form of the Fire Potential Index, to estimate two aspects of fire danger: 1) the probability that a 1 acre ignition will result in a 100+ acre fire, and 2) the probabilities of having at least 1, 2, 3, or 4 large fires within a Predictive Services Area in the forthcoming week. These statistical processes are the main thrust of the paper and are used to produce two daily national forecasts that are available from the U.S. Geological Survey, Earth Resources Observation and Science Center and via the Wildland Fire Assessment System. A validation study of our forecasts for the 2013 fire season demonstrated good agreement between observed and forecasted values.

Book↗
USGS2020

Bayesian modeling of non-stationary, univariate, spatial data for the Earth sciences

Some Earth science data, such as geochemical measurements of element concentrations, are non-stationary—the mean and the standard deviation vary spatially. It is important to estimate the spatial variations in both statistics because such information is indicative of geological and other Earth processes. To this end, an estimation method is formulated as a Bayesian hierarchical model. The method represents the spatially varying mean and the spatially varying standard deviation with basis functions; this formulation implicitly accounts for a spatially varying covariance function. A unique advantage of this method is that it can map the mean, the standard deviation, quantiles, and exceedance probabilities. The method is demonstrated by mapping titanium concentrations, which are measured in the coastal plain of the southeastern United States. Various checks demonstrate that the model fits the data and that the estimated statistics are geologically plausible.

Techniques and Methods↗
USGS2025

Bayesian mapping of regionally grouped, sparse, univariate earth science data

Some earth science data are naturally grouped by region, and it is often desirable to map these data by region. However, if there are only a few samples within each region, then the map should be smoothed in an appropriate way to mitigate the problems that arise from having only a few samples. A smoothing algorithm based on a Bayesian hierarchical model is developed and presented in this report. This algorithm has several features that make it especially suitable for mapping earth science data: it can account for measurements that are censored, it can process multiple datasets with different measurement errors and different censoring thresholds, and it can calculate the uncertainty in any statistic that is mapped. The algorithm is demonstrated by mapping gold concentrations that are measured in streambed sediments in the Taylor Mountains quadrangle in southwestern Alaska.

Alaska↗
USGS2010

Western Mineral and Environmental Resources Science Center--providing comprehensive earth science for complex societal issues

Minerals in the environment and products manufactured from mineral materials are all around us and we use and come into contact with them every day. They impact our way of life and the health of all that lives. Minerals are critical to the Nation's economy and knowing where future mineral resources will come from is important for sustaining the Nation's economy and national security. The U.S. Geological Survey (USGS) Mineral Resources Program (MRP) provides scientific information for objective resource assessments and unbiased research results on mineral resource potential, production and consumption statistics, as well as environmental consequences of mining. The MRP conducts this research to provide information needed for land planners and decisionmakers about where mineral commodities are known and suspected in the earth's crust and about the environmental consequences of extracting those commodities. As part of the MRP scientists of the Western Mineral and Environmental Resources Science Center (WMERSC or 'Center' herein) coordinate the development of national, geologic, geochemical, geophysical, and mineral-resource databases and the migration of existing databases to standard models and formats that are available to both internal and external users. The unique expertise developed by Center scientists over many decades in response to mineral-resource-related issues is now in great demand to support applications such as public health research and remediation of environmental hazards that result from mining and mining-related activities. Western Mineral and Environmental Resources Science Center Results of WMERSC research provide timely and unbiased analyses of minerals and inorganic materials to (1) improve stewardship of public lands and resources; (2) support national and international economic and security policies; (3) sustain prosperity and improve our quality of life; and (4) protect and improve public health, safety, and environmental quality. The MRP supports approximately 40 USGS research specialists who utilize cooperative agreements with universities, industry, and other governmental agencies to support their collaborative research and information exchange. Scientists of the WMERSC study how and where non-fuel mineral resources form and are concentrated in the earth's crust, where mineral resources might be found in the future, and how mineral materials interact with the environment to affect human and ecosystem health. Natural systems (ecosystems) are complex - our understanding of how ecosystems operate requires collecting and synthesizing large amounts of geologic, geochemical, biologic, hydrologic, and meteorological information. Scientists in the Center strive to understand the interplay of various processes and how they affect the structure, composition, and health of ecosystems. Such understanding, which is then summarized in publicly available reports, is used to address and solve a wide variety of issues that are important to society and the economy. WMERSC scientists have extensive national and international experience in these scientific specialties and capabilities - they have collaborated with many Federal, State, and local agencies; with various private sector organizations; as well as with foreign countries and organizations. Nearly every scientific and societal challenge requires a different combination of scientific skills and capabilities. With their breadth of scientific specialties and capabilities, the scientists of the WMERSC can provide scientifically sound approaches to a wide range of societal challenges and issues. The following sections describe examples of important issues that have been addressed by scientists in the Center, the methods employed, and the relevant conclusions. New directions are inevitable as societal needs change over time. Scientists of the WMERSC have a diverse set of skills and capabilities and are proficient in the collection and integration of

Circular↗
USGS2024

Predicting Lake Huron Dreissena spp. spatial distribution patterns from environmental characteristics

Invasive dreissenid mussels ( Dreissena polymorpha and Dreissena rostriformis bugensis ) have altered Great Lakes ecosystems through a multitude of effects on benthic habitats, food web structure, and nutrient cycling. This study explores whether spatially continuous geographic data of environmental factors can be utilized to predict Dreissena spp. spatial distributions on a lake-wide scale. Categorical variables were also assessed for significant relationships with Dreissena spp. biomass. Point observations from the 2017 Lake Huron benthic survey under the Cooperative Science and Monitoring Initiative (CSMI) were utilized for in situ measurements of dreissenid presence and biomass at 119 sites across Lake Huron. Basin, bathymetric zone, and tributary influence were found to have statistically significant relationships to dreissenid biomass. A boosted regression tree (BRT) model (ROC score 0.707) was developed to spatially predict dreissenid presence probability across Lake Huron from six environmental explanatory variables: April, May, and October chlorophyll, June dissolved organic carbon, January bottom temperature, and May bottom temperature. The importance of food availability and bottom temperature illuminated relationships between dreissenid mussels and periods of benthic-pelagic mixing in the spring and fall seasons. Future models could be improved through advancements in survey technology for improved geographic characterization of mussel habitat characteristics and environmental constraints.

Lake Huron↗
USGS2017

Optical and biochemical properties of a southwest Florida whiting event

“Whiting” in oceanography is a term used to describe a sharply defined patch of water that contains high levels of suspended, fine-grained calcium carbonate (CaCO 3 ). Whitings have been reported in many oceanic and lake environments, and recently have been reported in southwest Florida coastal waters. Here, field and laboratory measurements were used to study optical, biological, and chemical properties of whiting waters off southwest Florida. No significant difference was found in chlorophyll a concentrations between whiting and outside waters (non-whiting water), but average particle backscattering coefficients in whiting waters were double those in outside waters, and remote sensing reflectance in whiting waters was higher at all wavelengths (400–700 nm). While other potential causes cannot be completely ruled out, particle composition and biochemical differences between sampled whiting water, contiguous water, and outside water indicate a biologically precipitated mode of whiting formation. Taxonomic examination of marine phytoplankton samples collected during a whiting event revealed a community dominated by autotrophic picoplankton and a small (<10 μm), centric diatom species, identified as Thalassiosira sp. through the use of scanning electron microscopy. Amorphous to fully formed crystals of CaCO 3 were observed along the girdle bands of Thalassiosira sp. cells and autotrophic picoplankton cells. Although carbonate parameters differed from whiting and contiguous to outside water, more sampling is needed to determine if these results are statistically significant.

Florida↗
USGS2011

Paleohydrologic phenomena recorded by lake sediments

Hydrologic phenomena are dynamic, so their understanding and prediction are difficult and challenging. Many are cyclic, ranging from diurnal, seasonal, and annual cycles to climatic fluctuations of many hundreds or thousands of years. Predicting the magnitude and recurrence intervals of long-term fluctuations of these phenomena is a primary concern of hydrologists. The best data upon which to base predictions are those that directly measure a hydrologic phenomenon, such as stream discharge, lake level, and precipitation. Programs for collection of these data over a large area on a scientific basis, however, are less than 100 years old-not a very sound statistical base for predicting events that have recurrence intervals of at least that long.

Earth and Space Science↗
USGS2001

Statistics for wildlifers: how much and what kind?

Quantitative methods are playing increasingly important roles in wildlife ecology and, ultimately, management. This change poses a challenge for wildlife practitioners and students who are not well-educated in mathematics and statistics. Here we give our opinions on what wildlife biologists should know about statistics, while recognizing that not everyone is inclined mathematically. For those who are, we recommend that they take mathematics coursework at least through calculus and linear algebra. They should take statistics courses that are focused conceptually , stressing the Why rather than the How of doing statistics. For less mathematically oriented wildlifers, introductory classes in statistical techniques will furnish some useful background in basic methods but may provide little appreciation of when the methods are appropriate. These wildlifers will have to rely much more on advice from statisticians. Far more important than knowing how to analyze data is an understanding of how to obtain and recognize good data. Regardless of the statistical education they receive, all wildlife biologists should appreciate the importance of controls, replication, and randomization in studies they conduct. Understanding these concepts requires little mathematical sophistication, but is critical to advancing the science of wildlife ecology.

Wildlife Society Bulletin↗
USGS2001

Planetary Interactive GIS-on-the-Web Analyzable Database (PIGWAD)

The United States Geological Survey (USGS) in Flagstaff, Arizona is producing a Web-based, user-friendly interface that integrates powerful Geographic Information Systems (GIS) statistical and spatial relational tools for analyses of planetary datasets. The interface, known as “Planetary Interactive GIS-on-the-Web Analyzable Database” (PIGWAD), provides database support for the research and academic planetary science communities, particularly for geologic mapping and other surface-related investigations.

Conference Paper↗