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Results for “Environmental and Ecological Statistics”

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At least 37 records · Page 2Linked to original sources
USGS1997

Echelon approach to areas of concern in synoptic regional monitoring

Echelons provide an objective approach to prospecting for areas of potential concern in synoptic regional monitoring of a surface variable. Echelons can be regarded informally as stacked hill forms. The strategy is to identify regions of the surface which are elevated relative to surroundings ( R elative ELEVATIONS or RELEVATIONS ). These are areas which would continue to expand as islands with receding (virtual) floodwaters. Levels where islands would merge are critical elevations which delimit echelons in the vertical dimension. Families of echelons consist of surface sectors constituting separate islands for deeper waters that merge as water level declines. Pits which would hold water are disregarded in such a progression, but a complementary analysis of pits is obtained using the surface as a virtual mould to cast a counter-surface (bathymetric analysis). An echelon tree is a family tree of echelons with peaks as terminals and the lowest level as root. An echelon tree thus provides a dendrogram representation of surface topology which enables graph theoretic analysis and comparison of surface structures. Echelon top view maps show echelon cover sectors on the base plane. An echelon table summarizes characteristics of echelons as instances or cases of hill form surface structure. Determination of echelons requires only ordinal strength for the surface variable, and is thus appropriate for environmental indices as well as measurements. Since echelons are inherent in a surface rather than perceptual, they provide a basis for computer-intelligent understanding of surfaces. Echelons are given for broad-scale mammalian species richness in Pennsylvania.

Environmental and Ecological Statistics↗
USGS1997

Modelling heterogeneity in the recoveries of marked animal populations with covariates of individual animals, groups of animals or recovery time

A general framework is developed for modelling rates of survival and recovery of marked animal populations in terms of auxiliary information collected at the time of marking. The framework may be used to estimate differences in survival or recovery among individual animals, groups of animals, and recovery times. Analyses of the recoveries of tagged fish and banded bird populations are used to illustrate the specification and selection of various models.

Environmental and Ecological Statistics↗
USGS2003

Application of adaptive cluster sampling to low-density populations of freshwater mussels

Freshwater mussels appear to be promising candidates for adaptive cluster sampling because they are benthic macroinvertebrates that cluster spatially and are frequently found at low densities. We applied adaptive cluster sampling to estimate density of freshwater mussels at 24 sites along the Cacapon River, WV, where a preliminary timed search indicated that mussels were present at low density. Adaptive cluster sampling increased yield of individual mussels and detection of uncommon species; however, it did not improve precision of density estimates. Because finding uncommon species, collecting individuals of those species, and estimating their densities are important conservation activities, additional research is warranted on application of adaptive cluster sampling to freshwater mussels. However, at this time we do not recommend routine application of adaptive cluster sampling to freshwater mussel populations. The ultimate, and currently unanswered, question is how to tell when adaptive cluster sampling should be used, i.e., when is a population sufficiently rare and clustered for adaptive cluster sampling to be efficient and practical? A cost-effective procedure needs to be developed to identify biological populations for which adaptive cluster sampling is appropriate.

Environmental and Ecological Statistics↗
USGS2003

Longitudinal analysis of bioaccumulative contaminants in freshwater fishes

The National Contaminant Biomonitoring Program (NCBP) was initiated in 1967 as a component of the National Pesticide Monitoring program. It consists of periodic collection of freshwater fish and other samples and the analysis of the concentrations of persistent environmental contaminants in these samples. For the analysis, the common approach has been to apply the mixed two-way ANOVA model to combined data. A main disadvantage of this method is that it cannot give a detailed temporal trend of the concentrations since the data are grouped. In this paper, we present an alternative approach that performs a longitudinal analysis of the information using random effects models. In the new approach, no grouping is needed and the data are treated as samples from continuous stochastic processes, which seems more appropriate than ANOVA for the problem.

Environmental and Ecological Statistics↗
USGS2008

Efficient implementation of the Metropolis-Hastings algorithm, with application to the Cormack?Jolly?Seber model

Judicious choice of candidate generating distributions improves efficiency of the Metropolis-Hastings algorithm. In Bayesian applications, it is sometimes possible to identify an approximation to the target posterior distribution; this approximate posterior distribution is a good choice for candidate generation. These observations are applied to analysis of the Cormack?Jolly?Seber model and its extensions.

Environmental and Ecological Statistics↗
USGS2026

A weight-of-evidence system for integrating monitoring and environmental indicators to guide biological conservation and resource management of native taxa

Many state and federal environmental management agencies are charged with using data-driven evidence to conserve and restore threatened and endangered species that are adversely impacted by humans. Yet, statistical analyses of available environmental indicator data do not always promote unambiguous, ecologically-meaningful, evidence-based decisions for managing native biota. Here we modified our multi-step quantitative adaptive management framework to identify natural and anthropogenic environmental indicators that maintain or threaten native species. In a proof-of-concept example, we quantified how three native stream fish taxa responded to nine environmental indicators based on three criteria (i.e., statistical significance, ecological relevance, and agreement with expectations). The resulting Weight of Evidence (WoE) integration identified five groups of indicators, clarified ambiguous trends, and identified promising future directions that would not have emerged from examining a single analysis or a single taxon alone. Our evaluation and categorization of environmental indicators established the biological foundation on which multi-disciplinary sustainability rests. Our research contribution demonstrates how following a structured integration framework can lead to novel insights that benefit conservation across organisms and systems even when employing relatively simple analytical techniques. Our results address the following often ignored questions: How do we systematically link together different analytical components of the same larger problems? How do we ecologically integrate similar and dissimilar results across analyses? How can we identify generalities by comparing multiple taxa?

Kansas↗
USGS2018

The multiple-comparison trap and the Raven’s paradox—perils of using null hypothesis testing in environmental assessment

Detecting and quantifying environmental thresholds is frequently an important step in understanding ecological responses to environmental stressors. We discuss two statistical issues often encountered in threshold detection and quantification when statistical null hypothesis testing is used as a main analytical tool.The hidden multiple-comparison trap (leading to a much higher risk of a false detection) and Raven’s paradox(rendering a "detection" meaningless) are often obscured when statistical hypothesis testing is used as part of a more elaborate model, especially models based on computer-intensive methods. Using two examples, we show that the hidden multiple-comparison trap can be exposed using computer simulation to estimate the probability of making a false detection; Raven’s paradox can be avoided by clearly stating the null and alternative hypotheses using scientific terms to substantiate that the rejection of the null is equivalent to proving that the alternative of interest is true. The hidden multiple-comparison trap implies that a null hypothesis testing based on a computer-intensive method should be used with caution. The implication of Raven’s paradox re-quires that we focus on providing evidence supporting the proposed hypothesis or model, rather than seeking evidence against the frequently irrelevant null hypothesis. These two problems, and many others related to null hypothesis testing, suggest that statistical hypothesis testing should be used only as a component of the body of evidence, perhaps, as the devil’s advocate.

Environmental Monitoring and Assessment↗
USGS2006

A database of lotic invertebrate traits for North America

The assessment and study of stream communities may be enhanced if functional characteristics such as life-history, habitat preference, and reproductive strategy were more widely available for specific taxa. Species traits can be used to develop these functional indicators because many traits directly link functional roles of organisms with controlling environmental factors (for example, flow, substratum, temperature). In addition, some functional traits may not be constrained by taxonomy and are thus applicable at multiple spatial scales. Unfortunately, a comprehensive summary of traits for North American invertebrate taxa does not exist. Consequently, the U.S. Geological Survey's National Water-Quality Assessment Program in cooperation with Colorado State University compiled a database of traits for North American invertebrates. A total of 14,127 records for over 2,200 species, 1,165 genera, and 249 families have been entered into the database from 967 publications, texts and reports. Quality-assurance procedures indicated error rates of less than 3 percent in the data entry process. Species trait information was most complete for insect taxa. Traits describing resource acquisition and habitat preferences were most frequently reported, whereas those describing physiological tolerances and reproductive biology were the least frequently reported in the literature. The database is not exhaustive of the literature for North American invertebrates and is biased towards aquatic insects, but it represents a first attempt to compile traits in a web-accessible database. This report describes the database and discusses important decisions necessary for identifying ecologically relevant, environmentally sensitive, non-redundant, and statistically tractable traits for use in bioassessment programs.

Data Series↗
USGS2012

Experimental and environmental factors affect spurious detection of ecological thresholds

Threshold detection methods are increasingly popular for assessing nonlinear responses to environmental change, but their statistical performance remains poorly understood. We simulated linear change in stream benthic macroinvertebrate communities and evaluated the performance of commonly used threshold detection methods based on model fitting (piecewise quantile regression [PQR]), data partitioning (nonparametric change point analysis [NCPA]), and a hybrid approach (significant zero crossings [SiZer]). We demonstrated that false detection of ecological thresholds (type I errors) and inferences on threshold locations are influenced by sample size, rate of linear change, and frequency of observations across the environmental gradient (i.e., sample-environment distribution, SED). However, the relative importance of these factors varied among statistical methods and between inference types. False detection rates were influenced primarily by user-selected parameters for PQR (τ) and SiZer (bandwidth) and secondarily by sample size (for PQR) and SED (for SiZer). In contrast, the location of reported thresholds was influenced primarily by SED. Bootstrapped confidence intervals for NCPA threshold locations revealed strong correspondence to SED. We conclude that the choice of statistical methods for threshold detection should be matched to experimental and environmental constraints to minimize false detection rates and avoid spurious inferences regarding threshold location.

Ecology↗
USGS2011

Dynamics and spatio-temporal variability of environmental factors in Eastern Australia using functional principal component analysis

This paper introduces a new technique in ecology to analyze spatial and temporal variability in environmental variables. By using simple statistics, we explore the relations between abiotic and biotic variables that influence animal distributions. However, spatial and temporal variability in rainfall, a key variable in ecological studies, can cause difficulties to any basic model including time evolution. The study was of a landscape scale (three million square kilometers in eastern Australia), mainly over the period of 19982004. We simultaneously considered qualitative spatial (soil and habitat types) and quantitative temporal (rainfall) variables in a Geographical Information System environment. In addition to some techniques commonly used in ecology, we applied a new method, Functional Principal Component Analysis, which proved to be very suitable for this case, as it explained more than 97% of the total variance of the rainfall data, providing us with substitute variables that are easier to manage and are even able to explain rainfall patterns. The main variable came from a habitat classification that showed strong correlations with rainfall values and soil types. ?? 2010 World Scientific Publishing Company.

Journal of Biological Systems↗
USGS1996

Relations of fish community composition to environmental variables in streams of central Nebraska, USA

Nine sites on streams in the Platte River Basin in central Nebraska were sampled as part of the US Geological Survey's National Water Quality Assessment Program during 1993-1994. A combination of canonical correspondence analysis and an index of biotic integrity determined from fish community data produced complementary evaluations of water quality conditions. Results of the canonical correspondence analysis were useful in showing which environmental variables were significant in differentiating fish communities at the nine sites. Five environmental variables were statistically significant in the analysis. Median specific conductance of water samples collected at a site accounted for the largest amount of variability in the species data. Although the percentage of the basin as cropland was not the first variable chosen in a forward selection process, it was the most strongly correlated with the first ordination axis. A rangeland- dominated site was distinguished from all others along that axis. Median orthophosphate concentration of samples collected in the year up to the time of fish sampling was most strongly correlated with the second ordination axis. The index of biotic integrity produced results that could be interpreted in terms of the relative water quality between sites. Sites draining nearly 100% cropland had the lowest scores for two individual metrics of the index of biotic integrity that were related to species tolerance. Effective monitoring of water quality could be achieved by coupling methods that address both the ecological components of fish communities and their statistical relationships to environmental factors.

Environmental Management↗
USGS2022

Bayesian applications in environmental and ecological studies with R and Stan

Modern ecological and environmental sciences are dominated by observational data. As a result, traditional statistical training often leaves scientists ill-prepared for the data analysis tasks they encounter in their work. Bayesian methods provide a more robust and flexible tool for data analysis, as they enable information from different sources to be brought into the modelling process. Bayesian Applications in Evnironmental and Ecological Studies with R and Stan provides a Bayesian framework for model formulation, parameter estimation, and model evaluation in the context of analyzing environmental and ecological data. Features: An accessible overview of Bayesian methods in environmental and ecological studies Emphasizes the hypothetical deductive process, particularly model formulation Necessary background material on Bayesian inference and Monte Carlo simulation Detailed case studies, covering water quality monitoring and assessment, ecosystem response to urbanization, fisheries ecology, and more Advanced chapter on Bayesian applications, including Bayesian networks and a change point model Complete code for all examples, along with the data used in the book, are available via GitHub The book is primarily aimed at graduate students and researchers in the environmental and ecological sciences, as well as environmental management professionals. This is a group of people representing diverse subject matter fields, who could benefit from the potential power and flexibility of Bayesian methods.

Book↗
USGS2012

Using assemblage data in ecological indicators: A comparison and evaluation of commonly available statistical tools

Ecological indicators are science-based tools used to assess how human activities have impacted environmental resources. For monitoring and environmental assessment, existing species assemblage data can be used to make these comparisons through time or across sites. An impediment to using assemblage data, however, is that these data are complex and need to be simplified in an ecologically meaningful way. Because multivariate statistics are mathematical relationships, statistical groupings may not make ecological sense and will not have utility as indicators. Our goal was to define a process to select defensible and ecologically interpretable statistical simplifications of assemblage data in which researchers and managers can have confidence. For this, we chose a suite of statistical methods, compared the groupings that resulted from these analyses, identified convergence among groupings, then we interpreted the groupings using species and ecological guilds. When we tested this approach using a statewide stream fish dataset, not all statistical methods worked equally well. For our dataset, logistic regression (Log), detrended correspondence analysis (DCA), cluster analysis (CL), and non-metric multidimensional scaling (NMDS) provided consistent, simplified output. Specifically, the Log, DCA, CL-1, and NMDS-1 groupings were ≥60% similar to each other, overlapped with the fluvial-specialist ecological guild, and contained a common subset of species. Groupings based on number of species (e.g., Log, DCA, CL and NMDS) outperformed groupings based on abundance [e.g., principal components analysis (PCA) and Poisson regression]. Although the specific methods that worked on our test dataset have generality, here we are advocating a process (e.g., identifying convergent groupings with redundant species composition that are ecologically interpretable) rather than the automatic use of any single statistical tool. We summarize this process in step-by-step guidance for the future use of these commonly available ecological and statistical methods in preparing assemblage data for use in ecological indicators.

Ecological Indicators↗
USGS2018

Occupancy modeling species–environment relationships with non‐ignorable survey designs

Statistical models supporting inferences about species occurrence patterns in relation to environmental gradients are fundamental to ecology and conservation biology. A common implicit assumption is that the sampling design is ignorable and does not need to be formally accounted for in analyses. The analyst assumes data are representative of the desired population and statistical modeling proceeds. However, if data sets from probability and non‐probability surveys are combined or unequal selection probabilities are used, the design may be non‐ignorable. We outline the use of pseudo‐maximum likelihood estimation for site‐occupancy models to account for such non‐ignorable survey designs. This estimation method accounts for the survey design by properly weighting the pseudo‐likelihood equation. In our empirical example, legacy and newer randomly selected locations were surveyed for bats to bridge a historic statewide effort with an ongoing nationwide program. We provide a worked example using bat acoustic detection/non‐detection data and show how analysts can diagnose whether their design is ignorable. Using simulations we assessed whether our approach is viable for modeling data sets composed of sites contributed outside of a probability design. Pseudo‐maximum likelihood estimates differed from the usual maximum likelihood occupancy estimates for some bat species. Using simulations we show the maximum likelihood estimator of species–environment relationships with non‐ignorable sampling designs was biased, whereas the pseudo‐likelihood estimator was design unbiased. However, in our simulation study the designs composed of a large proportion of legacy or non‐probability sites resulted in estimation issues for standard errors. These issues were likely a result of highly variable weights confounded by small sample sizes (5% or 10% sampling intensity and four revisits). Aggregating data sets from multiple sources logically supports larger sample sizes and potentially increases spatial extents for statistical inferences. Our results suggest that ignoring the mechanism for how locations were selected for data collection (e.g., the sampling design) could result in erroneous model‐based conclusions. Therefore, in order to ensure robust and defensible recommendations for evidence‐based conservation decision‐making, the survey design information in addition to the data themselves must be available for analysts. Details for constructing the weights used in estimation and code for implementation are provided.

Ecological Applications↗
USGS2026

Space-based monitoring of penguin diet links sea ice, food webs, and population change

Rapid environmental change is reshaping Antarctic ecosystems through shifts in temperature and sea ice dynamics, with implications for species such as the iconic Adélie penguin ( Pygoscelis adeliae ). Because Antarctic predators rely on a relatively small number of key prey resources in the region 1 whose life histories are intrinsically linked to sea ice, 2 , 3 these abiotic changes are expected to cascade through food webs. However, the scale and remoteness of Antarctica have limited efforts to link environmental change to ecological responses across the continent. Combining tools from imaging spectroscopy, stable isotope analysis, and hierarchical statistical modeling, we reconstructed Adélie penguin diet across the entirety of the species’ global range over a three-decade period (1984–2013) using satellite imagery from the Landsat program. This approach leveraged the distinct spectral properties (a generalized measure of color) of penguin guano as observed by satellites, 4 , 5 which vary according to penguin diet. 6 , 7 We found pronounced differences in diet across the continent and that year-to-year dietary shifts were strongly associated with sea ice dynamics. Higher sea ice corresponded to more fish-based diets, whereas lower sea ice was associated with a greater reliance on krill. Additionally, spatial differences in penguin diet were associated with long-term trends in penguin abundance, linking food web processes to large-scale population dynamics. This study represents the first use of satellite observations to capture trophic dynamics at continental and decadal scales and highlights how environmental change might restructure Antarctic food webs and influence the future of a key sentinel species.

Current Biology↗
USGS1986

Benthic invertebrate population characteristics as affected by water quality in coal-bearing regions of Tennessee

Benthic invertebrate and water quality data collected during previous U.S. Geological Survey studies to provide background hydrologic information on streams draining Tennessee coal reserves, were evaluated to identify possible relations between stream biota and water quality. Linear regressions produced low correlation coefficients relating the number of taxa/sample, total number of organisms/sample, sample diversity, and percentage composition of selected orders of invertebrates, with average water quality parameter values available at sampling stations (r is < 0.62 at p=0.05). Analyses of these data by linear regressions explained little of the variability in benthic invertebrate samples primarily because the distributions of benthic organisms along environmental gradients are nonlinear. Variability in substrate characteristics in the study area and seasonal insect emergence patterns also complicated interpretation of these data. However, analysis of variance tests did indicate significant trends towards reduced number of taxa, number of organisms, and sample diversity at stations with relatively poor water quality conditions. Decreasing percentage composition of Ephemeroptera was generally accompanied by an increase in percent Diptera at stations with higher water quality constituent concentrations and acidic pH (> than 0.6 units). These trends indicate significant differences in benthic communities at sites with evidence of more severe land use impacts. Additional data on benthic invertebrates, water quality, and physical habitat conditions, along with analyses of data using multivariate statistical methods are needed to define ecological relations between specific groups of invertebrates and environmental conditions.

Tennessee↗
USGS2010

Bayesian Inference: with ecological applications

This text provides a mathematically rigorous yet accessible and engaging introduction to Bayesian inference with relevant examples that will be of interest to biologists working in the fields of ecology, wildlife management and environmental studies as well as students in advanced undergraduate statistics.. This text opens the door to Bayesian inference, taking advantage of modern computational efficiencies and easily accessible software to evaluate complex hierarchical models.

Book↗