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Results for “Environmental and Ecological Statistics”

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At least 55 records · Page 3Linked to original sources
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↗
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↗
USGS2007

Using simulated historical time series to prioritize fuel treatments on landscapes across the United States: The LANDFIRE prototype project

Canopy and surface fuels in many fire-prone forests of the United States have increased over the last 70 years as a result of modern fire exclusion policies, grazing, and other land management activities. The Healthy Forest Restoration Act and National Fire Plan establish a national commitment to reduce fire hazard and restore fire-adapted ecosystems across the USA. The primary index used to prioritize treatment areas across the nation is Fire Regime Condition Class (FRCC) computed as departures of current conditions from the historical fire and landscape conditions. This paper describes a process that uses an extensive set of ecological models to map FRCC from a departure statistic computed from simulated time series of historical landscape composition. This mapping process uses a data-driven, biophysical approach where georeferenced field data, biogeochemical simulation models, and spatial data libraries are integrated using spatial statistical modeling to map environmental gradients that are then used to predict vegetation and fuels characteristics over space. These characteristics are then fed into a landscape fire and succession simulation model to simulate a time series of historical landscape compositions that are then compared to the composition of current landscapes to compute departure, and the FRCC values. Intermediate products from this process are then used to create ancillary vegetation, fuels, and fire regime layers that are useful in the eventual planning and implementation of fuel and restoration treatments at local scales. The complex integration of varied ecological models at different scales is described and problems encountered during the implementation of this process in the LANDFIRE prototype project are addressed.

Ecological Modelling↗
USGS2007

Predicting the potential distribution of invasive exotic species using GIS and information-theoretic approaches: A case of ragweed (Ambrosia artemisiifolia L.) distribution in China

Invasive exotic species pose a growing threat to the economy, public health, and ecological integrity of nations worldwide. Explaining and predicting the spatial distribution of invasive exotic species is of great importance to prevention and early warning efforts. We are investigating the potential distribution of invasive exotic species, the environmental factors that influence these distributions, and the ability to predict them using statistical and information-theoretic approaches. For some species, detailed presence/absence occurrence data are available, allowing the use of a variety of standard statistical techniques. However, for most species, absence data are not available. Presented with the challenge of developing a model based on presence-only information, we developed an improved logistic regression approach using Information Theory and Frequency Statistics to produce a relative suitability map. This paper generated a variety of distributions of ragweed (Ambrosia artemisiifolia L.) from logistic regression models applied to herbarium specimen location data and a suite of GIS layers including climatic, topographic, and land cover information. Our logistic regression model was based on Akaike's Information Criterion (AIC) from a suite of ecologically reasonable predictor variables. Based on the results we provided a new Frequency Statistical method to compartmentalize habitat-suitability in the native range. Finally, we used the model and the compartmentalized criterion developed in native ranges to "project" a potential distribution onto the exotic ranges to build habitat-suitability maps. ?? Science in China Press 2007.

Chinese Science Bulletin↗
USGS2019

Predicting species distributions: unifying model selection and scale optimization for multi-scale occupancy models

Geographic distributions are a basic component of a species’ ecology, and predicting distributions is a fundamental task of conservation and resource management. Reliable prediction depends on identification of appropriate scales of effect for environmental data, and scale-optimization techniques are thus desirable to identify optimal scales for predictor variables. Recent statistical developments have also advanced methods of model selection based explicitly on predictive ability, which differ from commonly-used methods that regulate model structures via anticipated predictive performance. Such methods are beginning to permeate into species distribution models (SDMs), yet there remains no consensus methodology for developing optimally-predictive multi-scale SDMs when covariate data are collected over a range of scales. Thus, we compared the performance of common approaches for scale optimization and model selection in terms of their ability to produce optimally predictive multi-scale Bayesian occupancy models for predicting a species distribution, using models of the breeding distribution for King Rails (Rallus elegans) as a case study. Our results demonstrate sizable gains in predictive performance for hierarchical occupancy models selected explicitly via their ability to predict out-of-sample data using the logarithmic scoring rule, as compared to models selected using information criteria (DIC and WAIC). Information criteria commonly selected individual covariates, as well as scales of effect for those covariates, with suboptimal predictive performance. Performance of models selected using the logarithmic scoring rule was robust across method of scale optimization, which was not true for models selected using DIC and WAIC. Thus, we empirically demonstrate benefits of study designs that enable covariate and scale selection based explicitly on predictive ability. Our results also imply that more careful consideration of what constitutes an optimal scale is warranted in many ecological studies, as the meaning of optimal is not independent of the technique used for scale selection.

Ecosphere↗
USGS2019

Simultaneous autoregressive (SAR) model

Simultaneous autoregressive (SAR) models are useful for accommodating various forms of dependence among data that have discrete support in a space of interest. These models are often specified hierarchically as mixed-effects regression models with first-moment structure controlled by a conventional linear regression term and second-moment structure induced by correlated random effects. In their general form, SAR models resemble conditional autoregressive (CAR) models, and can be made equivalent but are often parameterized differently. Importantly, SAR models can be specified by simultaneously regressing a discrete spatial process on itself. Thus, they allow one to construct statistical models for processes with directional graphical properties that pertain to data generating mechanisms. Most commonly SAR models have been used to account for structure among data with areal spatial support in applications involving ecology, epidemiology, sociology, and environmental science.

Book chapter↗
USGS2003

Organic carbon trends, loads, and yields to the Sacramento-San Joaquin Delta, California, water years 1980 to 2000

Organic carbon, nutrient, and suspended sediment concentration data were analyzed for the Sacramento and San Joaquin River Basins for the period 1980-2000. The data were retrieved from three sources: the U.S. Geological Survey's National Water Information System, the U.S. Environmental Protection Agency's Storage and Retrieval System, and the California Interagency Ecological Program's relational database. Twenty sites were selected, all of which had complete records of daily streamflow data. These data met the minimal requirements of the statistical programs used to estimate trends, loads, and yields. The seasonal Kendall program was used to estimate trends in organic carbon, nutrient, and suspended sediment. At all 20 sites, analyses showed that in the 145 analyses for the seven constituents, 95 percent of the analyses had no significant trend. Dissolved organic carbon (DOC) concentrations were significant only for four sites: the American River at Sacramento, the Sacramento River sites near Freeport, Orestimba Creek at River Roads near Crows Landing, and the San Joaquin River near Vernalis. Loads were calculated using two programs, ESTIMATOR and LOADEST2. The 1998 water year was selected to describe loads in the Sacramento River Basin. Organic carbon, nutrient, and suspended sediment loads at the Sacramento River sites near Freeport included transported loads from two main upstream sites: the Sacramento River at Verona and the American River at Sacramento. Loads in the Sacramento River Basin were affected by the amount of water diverted to the Yolo Bypass (the amount varies annually, depending on the precipitation and streamflow). Loads at the Sacramento River sites near Freeport were analyzed for two hydrologic seasons: the irrigation season (April to September) and the nonirrigation season (October to March). DOC loads are lower during the irrigation season then they are during the nonirrigation season. During the irrigation season, water with low concentrations of DOC is released from reservoirs and used for irrigation. On the other hand, during the nonirrigation season, streamflow results from surface water runoff and has higher concentrations of organic carbon, nutrients, and suspended sediment. The 1986 and 1987 water years were selected to describe loads in the San Joaquin River Basin. Organic carbon, nutrient, and suspended sediment loads in the San Joaquin River near Vernalis included transported loads from upstream sites, such as the Mud and Salt Sloughs, the Merced River at River Roads Bridge near Newman, the Tuolumne River at Modesto, and the Stanislaus River at Ripon. Loads at the San Joaquin River near Vernalis also were analyzed for the two seasons. The DOC load for the San Joaquin River at Vernalis is slightly higher during the irrigation season. Yields were calculated in an attempt to rank the subbasins in the Sacramento and San Joaquin River Basins. Five sites delivered streamflow from agricultural and urban sources that had relatively high yields of organic carbon: Sacramento Slough near Knights Landing, Arcade Creek near Del Paso Heights, Salt Slough, Mud Slough, and Colusa Basin Drain at Road 99E near Knights Landing.

California↗
USGS2018

Generating community-built tools for data sharing and analysis in environmental networks

Rapid data growth in many environmental sectors has necessitated tools to manage and analyze these data. The development of tools often lags behind the proliferation of data, however, which may slow exploratory opportunities and scientific progress. The Global Lake Ecological Observatory Network (GLEON) collaborative model supports an efficient and comprehensive data–analysis–insight life cycle, including implementations of data quality control checks, statistical calculations/derivations, models, and data visualizations. These tools are community-built and openly shared. We discuss the network structure that enables tool development and a culture of sharing, leading to optimized output from limited resources. Specifically, data sharing and a flat collaborative structure encourage the development of tools that enable scientific insights from these data. Here we provide a cross-section of scientific advances derived from global-scale analyses in GLEON. We document enhancements to science capabilities made possible by the development of analytical tools and highlight opportunities to expand this framework to benefit other environmental networks.

Inland Waters↗
USGS2017

Addressing wild turkey population declines using structured decision making

We present a case study from New York, USA, of the use of structured decision making (SDM) to identify fall turkey harvest regulations that best meet stakeholder objectives, in light of recent apparent declines in abundance of wild turkeys in the northeastern United States. We used the SDM framework to incorporate the multiple objectives associated with turkey hunting, stakeholder desires, and region-specific ecological and environmental factors that could influence fall harvest. We identified a set of 4 fall harvest regulations, composed of different season lengths and bag limits, and evaluated their relative achievement of the objectives. We used a stochastic turkey population model, statistical modeling, and expert elicitation to evaluate the consequences of each harvest regulation on each of the objectives. We conducted a statewide mail survey of fall turkey hunters in New York to gather the necessary information to evaluate tradeoffs among multiple objectives associated with hunter satisfaction. The optimal fall harvest regulation was a 2-week season and allowed for the harvest of 1 bird/hunter. This regulation was the most conservative of those evaluated, reflecting the concerns about recent declines in turkey abundance among agency wildlife biologists and the hunting public. Depending on the region of the state, the 2-week, 1-bird regulation was predicted to result in 7–32% more turkeys on the landscape after 5 years. The SDM process provided a transparent framework for setting fall turkey harvest regulations and reduced potential stakeholder conflict by explicitly taking the multiple objectives of different stakeholder groups into account.

New York↗
USGS2013

Ecological limit functions relating fish community response to hydrologic departures of the ecological flow regime in the Tennessee River basin, United States

Ecological limit functions relating streamflow and aquatic ecosystems remain elusive despite decades of research. We investigated functional relationships between species richness and changes in streamflow characteristics at 662 fish sampling sites in the Tennessee River basin. Our approach included the following: (1) a brief summary of relevant literature on functional relations between fish and streamflow, (2) the development of ecological limit functions that describe the strongest discernible relationships between fish species richness and streamflow characteristics, (3) the evaluation of proposed definitions of hydrologic reference conditions, and (4) an investigation of the internal structures of wedge-shaped distributions underlying ecological limit functions. Twenty-one ecological limit functions were developed across three ecoregions that relate the species richness of 11 fish groups and departures from hydrologic reference conditions using multivariate and quantile regression methods. Each negatively sloped function is described using up to four streamflow characteristics expressed in terms of cumulative departure from hydrologic reference conditions. Negative slopes indicate increased departure results in decreased species richness. Sites with the highest measured fish species richness generally had near-reference hydrologic conditions for a given ecoregion. Hydrology did not generally differ between sites with the highest and lowest fish species richness, indicating that other environmental factors likely limit species richness at sites with reference hydrology. Use of ecological limit functions to make decisions regarding proposed hydrologic regime changes, although commonly presented as a management tool, is not as straightforward or informative as often assumed. We contend that statistical evaluation of the internal wedge structure below limit functions may provide a probabilistic understanding of how aquatic ecology is influenced by altered hydrology and may serve as the basis for evaluating the potential effect of proposed hydrologic changes.

Tennessee River basin↗