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At least 37 records · Page 2Linked to original sources
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↗
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↗
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↗
USGS2005

Development of ecological indicator guilds for land management

Agency land-use must be efficiently and cost-effectively monitored to assess conditions and trends in ecosystem processes and natural resources relevant to mission requirements and legal mandates. Ecological Indicators represent important land management tools for tracking ecological changes and preventing irreversible environmental damage in disturbed landscapes. The overall objective of the research was to develop both individual and integrated sets (i.e., statistically derived guilds) of Ecological Indicators to: quantify habitat conditions and trends, track and monitor ecological changes, provide early warning or threshold detection, and provide guidance for land managers. The derivation of Ecological Indicators was based on statistical criteria, ecosystem relevance, reliability and robustness, economy and ease of use for land managers, multi-scale performance, and stress response criteria. The basis for the development of statistically based Ecological Indicators was the identification of ecosystem metrics that analytically tracked a landscape disturbance gradient.

Report↗
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↗
USGS2014

Please don't misuse the museum: 'declines' may be statistical

Detecting declines in populations at broad spatial scales takes enormous effort, and long-term data are often more sparse than is desired for estimating trends, identifying drivers for population changes, framing conservation decisions or taking management actions. Museum records and historic data can be available at large scales across multiple decades, and are therefore an attractive source of information on the comparative status of populations. However, changes in populations may be real (e.g., in response to environmental covariates) or resulting from variation in our ability to observe the true population response (also possibly related to environmental covariates). This is a (statistical) nuisance in understanding the true status of a population. Evaluating statistical hypotheses alongside more interesting ecological ones is important in the appropriate use of museum data. Two statistical considerations are generally applicable to use of museum records: first without initial random sampling, comparison with contemporary results cannot provide inference to the entire range of a species, and second the availability of only some individuals in a population may respond to environmental changes. Changes in the availability of individuals may reduce the proportion of the population that is present and able to be counted on a given survey event, resulting in an apparent decline even when population size is stable.

Global Change Biology↗
USGS2010

Breakpoint analysis and relations of nutrient and turbidity stressor variables to macroinvertebrate integrity in streams in the Crawford-Mammoth Cave Uplands Ecoregion, Kentucky, for the development of nutrient criteria

To assist Kentucky in refining numeric nutrient criteria in the Pennyroyal Bioregion, the U.S. Geological Survey and the Kentucky Division of Water collected and analyzed water chemistry, turbidity, and biological-community data from 22 streams throughout the Crawford-Mammoth Cave Upland ecoregion (U.S. Environmental Protection Agency Level IV Ecoregion, 71a) within the Pennyroyal Bioregion from September 2007 to May 2008. Statistically significant and ecologically relevant relations among the stressor (total phosphorus, total nitrogen, and turbidity) variables and response (macroinvertebrate-community attributes) variables and the breakpoint values of biological-community attributes and metrics in response to changes in stressor variables were determined. Thirteen of 18 macroinvertebrate attributes were significantly and ecologically correlated (p-value < 0.10) with at least one nutrient measure. Total number of individuals, Ephemeroptera-Plecoptera-Trichoptera richness, and average tolerance value were macroinvertebrate measures that most strongly correlated with the concentrations of nutrients. Comparison of the average macroinvertebrate-breakpoint value for the median concentration of total phosphorus (TP, 0.033 mg/L) and for median concentration of total nitrogen (TN, 1.1 mg/L) to Dodds' trophic classification for TP and TN indicates streams in the Crawford-Mammoth Cave Uplands ecoregion within the Pennyroyal Bioregion would be classified as mesotrophic-eutrophic. The biological breakpoint relations with median concentrations of TP in this study were similar to the U.S. Environmental Protection Agency proposed numeric TP criteria (0.037 mg/L), but were 1.5 times higher than the proposed numeric criteria for concentrations of TN (0.69 mg/L). No sites were impacted adversely using median turbidity values based on a 25 Formazin nephelometric turbidity unit biological threshold. The breakpoints determined in this study, in addition to Dodds' trophic classifications, were used as multiple lines of evidence to show changes in macroinvertebrate community and attributes based on exposure to nutrients.

Kentucky↗
USGS2002

Generalized linear and generalized additive models in studies of species distributions: Setting the scene

An important statistical development of the last 30 years has been the advance in regression analysis provided by generalized linear models (GLMs) and generalized additive models (GAMs). Here we introduce a series of papers prepared within the framework of an international workshop entitled: Advances in GLMs/GAMs modeling: from species distribution to environmental management, held in Riederalp, Switzerland, 6-11 August 2001. We first discuss some general uses of statistical models in ecology, as well as provide a short review of several key examples of the use of GLMs and GAMs in ecological modeling efforts. We next present an overview of GLMs and GAMs, and discuss some of their related statistics used for predictor selection, model diagnostics, and evaluation. Included is a discussion of several new approaches applicable to GLMs and GAMs, such as ridge regression, an alternative to stepwise selection of predictors, and methods for the identification of interactions by a combined use of regression trees and several other approaches. We close with an overview of the papers and how we feel they advance our understanding of their application to ecological modeling. ?? 2002 Elsevier Science B.V. All rights reserved.

Ecological Modelling↗
USGS2016

Incorporating imperfect detection into joint models of communites: A response to Warton et al.

Warton et al. [1] advance community ecology by describing a statistical framework that can jointly model abundances (or distributions) across many taxa to quantify how community properties respond to environmental variables. This framework specifies the effects of both measured and unmeasured (latent) variables on the abundance (or occurrence) of each species. Latent variables are random effects that capture the effects of both missing environmental predictors and correlations in parameter values among different species. As presented in Warton et al. , however, the joint modeling framework fails to account for the common problem of detection or measurement errors that always accompany field sampling of abundance or occupancy, and are well known to obscure species- and community-level inferences.

Trends in Ecology and Evolution↗