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At least 55 records · Page 3Linked to original sources
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↗
USGS2021

Adaptive monitoring in action: Reconsidering design-based estimators reveals underestimation of whitebark pine disease prevalence in the Greater Yellowstone Ecosystem

Identifying and understanding status and trends in ecological indicators motivates continual monitoring over decades. Many programs rely on probability surveys and their companion design-based estimators for status assessments (e.g. Horvitz–Thompson). Design-based estimators do not easily extend to trend estimation nor situations with observation errors. Field-based monitoring efforts inevitably have turnover of field crew members which may affect consistency and accuracy of data collection over time. Additionally, design-based estimators ignore the complexities of spatial and temporal heterogeneity in an ecological indicator and how this variability may be linked to environmental or biological dynamics. We propose monitoring programs should re-evaluate their prescribed statistical methods, consider model-based approaches and adapt their sampling designs as needed to improve inferences. The Greater Yellowstone Ecosystem, home to two of the most iconic U.S. National Parks, has experienced significant declines in whitebark pine Pinus albicaulis communities due to forest pathogens, insect outbreaks, wildland fires and drought. Whitebark pine is a keystone species found in mountainous environments throughout the Western U.S. and Canada. We assessed the design-based ratio estimator originally recommended for estimating prevalence of white pine blister rust Cronartium ribicola . We compared the design-based estimator to a model-based approach that accounts for the sampling design, imperfect detection and allows for infection probabilities to vary over space and time. Ignoring observation errors led to lower estimated prevalence of white pine blister rust in the general population. Using model-based approaches, we found that the probability of infection has increased since 2004. However, overall prevalence likely has not changed because of the mountain pine beetle Dendroctonus ponderosae -induced shift towards smaller diameter trees that have a lower probability of infection compared to their larger cohorts. Synthesis and Applications . Using a design-based approach to detect change in ecological indicators falls short because of the inability to account for observation errors or to explore environmental or biological factors explaining temporal dynamics. Inherently understanding the mechanisms leading to changes in an ecological indicator over time informs potential management actions. Our assessment underscores the need for continued evaluation and updating of a monitoring program's sampling design and analytical procedures to maintain relevancy.

Wyoming↗
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↗
USGS1997

The sandflat habitat: Scaling from experiments to conclusions

Ecological systems are characterised by spatial and temporal variations in the density of organisms and resources, and in the intensity of processes which affect them (e.g. Watt, 1947, Dayton, 1971, Addicot et al., 1987, Kolasa and Pickett, 1991 and Giller et al., 1994). This heterogeneity represents both a difficulty for field study design and statistical testing, and a challenge to describe the spatial structuring of populations, communities and ecosystems (Legendre, 1993). Patterns in ecological and environmental variables are fundamental to developing hypotheses and in designing subsequent field studies, because they determine the spatial and temporal scales of study. Spatial and/or temporal patterns also provide useful stepping stones for addressing issues of scale. Patterns apparent at one scale can collapse to noise when viewed from other scales, indicating that patterns, processes and our perceptions vary in a scale-dependent manner. Perhaps the most important but rarely discussed issue is the question of how to scale-up from small-scale surveys and experiments to conclusions relevant at larger spatial and temporal scales.

Conference Paper↗
USGS2005

Ground-water vulnerability to nitrate contamination in the mid-atlantic region

The U.S. Environmental Protection Agency?s (USEPA) Regional Vulnerability Assessment (ReVA) Program has developed a set of statistical tools to support regional-scale, integrated ecological risk-assessment studies. One of these tools, developed by the U.S. Geological Survey (USGS), is used with available water-quality data obtained from USGS National Water-Quality Assessment (NAWQA) and other studies in association with land cover, geology, soils, and other geographic data to develop logistic-regression equations that predict the vulnerability of ground water to nitrate concentrations exceeding specified thresholds in the Mid-Atlantic Region. The models were developed and applied to produce spatial probability maps showing the likelihood of elevated concentrations of nitrate in the region. These maps can be used to identify areas that currently are at risk and help identify areas where ground water has been affected by human activities. This information can be used by regional and local water managers to protect water supplies and identify land-use planning solutions and monitoring programs in these vulnerable areas.

Fact Sheet↗
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↗
USGS2012

Downscaling future climate scenarios to fine scales for hydrologic and ecological modeling and analysis

Introduction Evaluating the environmental impacts of climate change on water resources and biological components of the landscape is an integral part of hydrologic and ecological investigations, and the resultant land and resource management in the twenty-first century. Impacts of both climate and simulated hydrologic parameters on ecological processes are relevant at scales that reflect the heterogeneity and complexity of landscapes. At present, simulations of climate change available from global climate models [GCMs] require downscaling for hydrologic or ecological applications. Methods Using statistically downscaled future climate projections developed using constructed analogues, a methodology was developed to further downscale the projections spatially using a gradient-inverse-distance-squared approach for application to hydrologic modeling at 270-m spatial resolution. Results This paper illustrates a methodology to downscale and bias-correct national GCMs to subkilometer scales that are applicable to fine-scale environmental processes. Four scenarios were chosen to bracket the range of future emissions put forth by the Intergovernmental Panel on Climate Change. Fine-scale applications of downscaled datasets of ecological and hydrologic correlations to variation in climate are illustrated. Conclusions The methodology, which includes a sequence of rigorous analyses and calculations, is intended to reduce the addition of uncertainty to the climate data as a result of the downscaling while providing the fine-scale climate information necessary for ecological analyses. It results in new but consistent data sets for the US at 4 km, the southwest US at 270 m, and California at 90 m and illustrates the utility of fine-scale downscaling to analyses of ecological processes influenced by topographic complexity.

Ecological Processes↗
USGS2014

Assessing and managing freshwater ecosystems vulnerable to global change

Freshwater ecosystems are important for global biodiversity and provide essential ecosystem services. There is consensus in the scientific literature that freshwater ecosystems are vulnerable to the impacts of environmental change, which may trigger irreversible regime shifts upon which biodiversity and ecosystem services may be lost. There are profound uncertainties regarding the management and assessment of the vulnerability of freshwater ecosystems to environmental change. Quantitative approaches are needed to reduce this uncertainty. We describe available statistical and modeling approaches along with case studies that demonstrate how resilience theory can be applied to aid decision-making in natural resources management. We highlight especially how long-term monitoring efforts combined with ecological theory can provide a novel nexus between ecological impact assessment and management, and the quantification of systemic vulnerability and thus the resilience of ecosystems to environmental change.

Florida Everglades↗
USGS2009

A Chronosequence Feasibility Assessment of Emergency Fire Rehabilitation Records within the Intermountain Western United States - Final Report to the Joint Fire Science Program - Project 08-S-08

Department of the Interior (DOI) bureaus have invested heavily (for example, the U.S. Bureau of Land Management (BLM) spent more than $60 million in fiscal year 2007) in seeding vegetation for emergency stabilization and burned area rehabilitation of non-forested arid lands over the past 10 years. The primary objectives of these seedings commonly are to (1) reduce the post-fire dominance of non-native annual grasses, such as cheatgrass (Bromus tectorum) and red brome (Bromus rubens); (2) minimize the probability of recurrent fire; and (3) ultimately produce desirable vegetation characteristics (for example, ability to recover following disturbance [resilience], resistance to invasive species, and a capacity to support a diverse flora and fauna). Although these projects historically have been monitored to varying extents, land managers currently lack scientific evidence to verify whether seeding arid and semiarid lands achieves desired objectives. Given the amount of resources dedicated to post-fire seeding projects, a synthesis of information determining the factors that result in successful treatments is critically needed. Although results of recently established experiments and monitoring projects eventually will provide useful insights for the future direction of emergency stabilization and burned area rehabilitation programs, a chronosequence approach evaluating emergency stabilization and burned area rehabilitation treatments (both referenced hereafter as ESR treatments) over the past 30 years could provide a comprehensive assessment of treatment success across a range of regional environmental gradients. By randomly selecting a statistically robust sample from the population of historic ESR treatments in the Intermountain West, this chronosequence approach would have inference for most ecological sites in this region. The goal of this feasibility study was to compile and examine historic ESR records from BLM field offices across the Intermountain West to determine whether sufficient documentation existed for a future field-based chronosequence project. We collected ESR records and data at nine BLM field offices in four States (Oregon, Idaho, Nevada, and Utah) and examined the utility of these data for the development of a chronosequence study of post-fire seeding treatments from multiple sites and different ages (since seeding) throughout the Intermountain West. We collected records from 730 post-fire seeding projects with 1,238 individual seeding treatments. Records from each project ranged from minimal reporting of the project's occurrence to detailed documentation of planning, implementation, and monitoring. Of these 1,238 projects, we identified 468 (38 percent) that could potentially be used to implement a field-based chronosequence study. There were 206 ground-seeding treatments and 262 aerial-seeding treatments within this initial population, not including hand plantings. We also located a considerable number of additional records from other potential field offices that would be available for the chronosequence study but have yet to be compiled for this feasibility report. There are a number of potential challenges involved in going forward with a field-based chronosequence study derived from data collected at these nine BLM offices. One challenge is that not all seed mixtures in ESR project files have on-the-ground confirmation about what was sown or rates of application. Most projects, particularly records before 2000, just list the planned or purchased seed mixtures. Although this could potentially bias assessments of factors influencing establishment rates of individual species for treatments conducted before 2000, a chronosequence study would not be intended to assess success solely at the species-level. Treatment success would be evaluated based on the establishment of healthy vegetation communities, such as the abundance and density of perennial species, regardless of their lifeforms (grasses, fo

Open-File Report↗
USGS2008

Modeling potential habitats for alien species Dreissena polymorpha in continental USA

The effective measure to minimize the damage of invasive species is to block the potential invasive species to enter into suitable areas. 1864 occurrence points with GPS coordinates and 34 environmental variables from Daymet datasets were gathered, and 4 modeling methods, i.e., Logistic Regression (LR), Classification and Regression Trees (CART), Genetic Algorithm for Rule-Set Prediction (GARP), and maximum entropy method (Maxent), were introduced to generate potential geographic distributions for invasive species Dreissena polymorpha in Continental USA. Then 3 statistical criteria of the area under the Receiver Operating Characteristic curve (AUC), Pearson correlation (COR) and Kappa value were calculated to evaluate the performance of the models, followed by analyses on major contribution variables. Results showed that in terms of the 3 statistical criteria, the prediction results of the 4 ecological niche models were either excellent or outstanding, in which Maxent outperformed the others in 3 aspects of predicting current distribution habitats, selecting major contribution factors, and quantifying the influence of environmental variables on habitats. Distance to water, elevation, frequency of precipitation and solar radiation were 4 environmental forcing factors. The method suggested in the paper can have some reference meaning for modeling habitats of alien species in China and provide a direction to prevent Mytilopsis sallei on the Chinese coast line.

Acta Ecologica Sinica↗
USGS2012

A high-resolution bioclimate map of the world: a unifying framework for global biodiversity research and monitoring

Aim: To develop a novel global spatial framework for the integration and analysis of ecological and environmental data. Location: The global land surface excluding Antarctica. Methods: A broad set of climate-related variables were considered for inclusion in a quantitative model, which partitions geographic space into bioclimate regions. Statistical screening produced a subset of relevant bioclimate variables, which were further compacted into fewer independent dimensions using principal components analysis (PCA). An ISODATA clustering routine was then used to classify the principal components into relatively homogeneous environmental strata. The strata were aggregated into global environmental zones based on the attribute distances between strata to provide structure and support a consistent nomenclature. Results: The global environmental stratification (GEnS) consists of 125 strata, which have been aggregated into 18 global environmental zones. The stratification has a 30 arcsec resolution (equivalent to 0.86 km2 at the equator). Aggregations of the strata were compared with nine existing global, continental and national bioclimate and ecosystem classifications using the Kappa statistic. Values range between 0.54 and 0.72, indicating good agreement in bioclimate and ecosystem patterns between existing maps and the GEnS. Main conclusions: The GEnS provides a robust spatial analytical framework for the aggregation of local observations, identification of gaps in current monitoring efforts and systematic design of complementary and new monitoring and research. The dataset is available for non-commercial use through the GEO portal (http://www.geoportal.org).

Global Ecology and Biogeography↗
USGS2003

Effects of two classification strategies on a Benthic Community Index for streams in the Northern Lakes and Forests Ecoregion

Ninety-four sites were used to analyze the effects of two different classification strategies on the Benthic Community Index (BCI). The first, a priori classification, reflected the wetland status of the streams; the second, a posteriori classification, used a bio-environmental analysis to select classification variables. Both classifications were examined by measuring classification strength and testing differences in metric values with respect to group membership. The a priori (wetland) classification strength (83.3%) was greater than the a posteriori (bio-environmental) classification strength (76.8%). Both classifications found one metric that had significant differences between groups. The original index was modified to reflect the wetland classification by re-calibrating the scoring criteria for percent Crustacea and Mollusca. A proposed refinement to the original Benthic Community Index is suggested. This study shows the importance of using hypothesis-driven classifications, as well as exploratory statistical analysis, to evaluate alternative ways to reveal environmental variability in biological assessment tools.

Ecological Indicators↗
USGS2012

Linking urbanization to the Biological Condition Gradient (BCG) for stream ecosystems in the Northeastern United States using a Bayesian network approach

Urban development alters important physical, chemical, and biological processes that define urban stream ecosystems. An approach was developed for quantifying the effects of these processes on aquatic biota, and then linking those effects to endpoints that can be used for environmental management. These complex, interacting systems are challenging to model from a scientific standpoint. A desirable model clearly shows the system, simulates the interactions, and ultimately predicts results of management actions. Traditional regression techniques that calculate empirical relations between pairs of environmental factors do not capture the interconnected web of multiple stressors, but urban development effects are not yet understood at the detailed scales required to make mechanistic modeling approaches feasible. Therefore, in contrast to a fully deterministic or fully statistical modeling approach, a Bayesian network model provides a hybrid approach that can be used to represent known general associations between variables while acknowledging uncertainty in predicted outcomes. It does so by quantifying an expert-elicited network of probabilistic relations between variables. Advantages of this modeling approach include (1) flexibility in accommodating many model specifications and information types; (2) efficiency in storing and manipulating complex information, and to parameterize; and (3) transparency in describing the relations using nodes and arrows and in describing uncertainties with discrete probability distributions for each variable. In realization of the aforementioned advantages, a Bayesian network model was constructed to characterize the effect of urban development on aquatic macroinvertebrate stream communities through three simultaneous, interacting ecological pathways affecting stream hydrology, habitat, and water quality across watersheds in the Northeastern United States. This model incorporates both empirical data and expert knowledge to calculate the probabilities of attaining desired aquatic ecosystem conditions under different urban stress levels, environmental conditions, and management options. Ecosystem conditions are characterized in terms of standardized Biological Condition Gradient (BCG) management endpoints. This approach to evaluating urban development-induced perturbations in watersheds integrates statistical and mechanistic perspectives, different information sources, and several ecological processes into a comprehensive description of the system that can be used to support decision making. The completed model can be used to infer which management actions would lead to the highest likelihood of desired BCG tier achievement. For example, if best management practices (BMP) were implemented in a highly urbanized watershed to reduce flashiness to medium levels and specific conductance to low levels, the stream would have a 70-percent chance of achieving BCG Tier 3 or better, relative to a 24-percent achievement likelihood for unmanaged high urban land cover. Results are reported probabilistically to account for modeling uncertainty that is inherent in sources such as natural variability and model simplification error.

Scientific Investigations Report↗