Jeffrey W. Hollister

Assessing the Accuracy of National Land Cover Dataset Area Estimates at Multiple Spatial Extents

Site-specific accuracy assessments evaluate fine-scale accuracy of land-use/land-cover (LULC) datasets but provide little insight into accuracy of area estimates of LULC classes derived from sampling units of varying size. Additionally, accuracy of landscape structure metrics calculated from area estimates cannot be determined solely from site-specific assessments. We used LULC data from Rhode Island and Massachusetts as reference to determine the accuracy of area measurements from the National Land Cover Dataset (NLCD) within spatial units ranging from 0.1 to 200 km 2 . When regressed on reference area, NLCD area of developed land, agriculture, forest, and water had positive linear relationships with high r2, suggesting acceptable accuracy. However, many of these classes also displayed mean differences (NLCD - REFERENCE), and linear relationships between the NLCD and reference were not one-to-one (i.e., low r 2 , β 0 ¬= 0, β 1 ¬= 1), suggesting mapped area is different from true area. Rangeland, wetland, and barren were consistently, poorly classified.

Using GIS to Estimate Lake Volume from Limited Data

Abstract Estimates of lake volume are necessary for calculating residence time and modeling pollutants. Modern GIS methods for calculating lake volume improve on more dated technologies (e.g., planimeters) and do not require potentially inaccurate assumptions (e.g., volume of a frustum of a cone), but most GIS methods do require detailed bathymetric data, which may be unavailable. GIS technology cannot correct for a lack of data; however, it can facilitate development of methods that better use the relatively simple and more widely available measurements of lake shape and maximum depth. In this research note we describe a method to model bathymetry and estimate lake volume with a limited set of data that consists only of a maximum depth measurement and a GIS layer of lake shoreline. Using a simple linear transformation, we estimated depth as a function of distance from shoreline and with the resultant information estimated lake volume. We applied and compared this method with estimates derived from field bathymetry data of 129 lakes in New Hampshire. In New Hampshire lakes, the assumption of depth as a function of distance is appropriate, and the simple GIS method has lower overall error than simply using the formula for volume of a cone to estimate lake volume. This approach has broad implications in the assessment of lake condition from national surveys (e.g., US Environmental Protection Agencys National Lakes Assessment) and should improve upon models of nutrients, contaminants and hydrology, even in the absence of detailed bathymetric data. Supplemental materials are available for this article. Go to the publishers online edition of Lake and Reservoir Management to view the free supplemental file.

Predicting Maximum Lake Depth from Surrounding Topography

Information about lake morphometry (e.g., depth, volume, size, etc.) aids understanding of the physical and ecological dynamics of lakes, yet is often not readily available. The data needed to calculate measures of lake morphometry, particularly lake depth, are usually collected on a lake-by-lake basis and are difficult to obtain across broad regions. To span the gap between studies of individual lakes where detailed data exist and regional studies where access to useful data on lake depth is unavailable, we developed a method to predict maximum lake depth from the slope of the topography surrounding a lake. We use the National Elevation Dataset and the National Hydrography Dataset – Plus to estimate the percent slope of surrounding lakes and use this information to predict maximum lake depth. We also use field measured maximum lake depths from the US EPAs National Lakes Assessment to empirically adjust and cross-validate our predictions. We were able to predict maximum depth for ∼28,000 lakes in the Northeastern United States with an average cross-validated RMSE of 5.95 m and 5.09 m and average correlation of 0.82 and 0.69 for Hydrological Unit Code Regions 01 and 02, respectively. The depth predictions and the scripts are openly available as supplements to this manuscript.

Effects of spatial extent on landscape structure and sediment metal concentration relationships in small estuarine systems of the United States’ Mid-Atlantic Coast

Prior studies exploring the quantitative relationship between landscape structure metrics and the ecological condition of receiving waters have used a variety of sampling units (e.g., a watershed, or a buffer around a sampling station) at a variety of spatial scales to generate landscape metrics resulting in little consensus on which scales best describe land-water relationships. Additionally, the majority of these studies have focused on freshwater systems and it is not clear whether results are transferable to estuarine and marine systems. We examined how sampling unit scale controls the relationship between landscape structure and sediment metal concentrations in small estuarine systems in the Mid-Atlantic region of the United States. We varied the spatial extent of the contributing watersheds used to calculate landscape structure and assessed linear relationships between estuarine sediment metal concentrations and the total area of developed and agricultural lands at each scale. Area of developed lands was consistently related to sediment metals while total agricultural land was not. Developed land had strongest associations with lead and copper; weakest with arsenic and chromium; and moderate associations with cadmium, mercury, and zinc. Local (i.e., less than 15−20 km from a sampling station) land uses have a greater impact than more distant land uses on the amount of toxic metals reaching estuarine sediments.

Socioeconomic Factors Affecting Local Support for Black Bear Recovery Strategies

There is global interest in recovering locally extirpated carnivore species. Successful efforts to recover Louisiana black bear in Louisiana have prompted interest in recovery throughout the species’ historical range. We evaluated support for three potential black bear recovery strategies prior to public release of a black bear conservation and management plan for eastern Texas, United States. Data were collected from 1,006 residents living in proximity to potential recovery locations, particularly Big Thicket National Preserve. In addition to traditional logistic regression analysis, we used conditional probability analysis to statistically and visually evaluate probabilities of public support for potential black bear recovery strategies based on socioeconomic characteristics. Allowing black bears to repopulate the region on their own (i.e., without active reintroduction) was the recovery strategy with the greatest probability of acceptance. Recovery strategy acceptance was influenced by many socioeconomic factors. Older and long-time local residents were most likely to want to exclude black bears from the area. Concern about the problems that black bears may cause was the only variable significantly related to support or non-support across all strategies. Lack of personal knowledge about black bears was the most frequent reason for uncertainty about preferred strategy. In order to reduce local uncertainty about possible recovery strategies, we suggest that wildlife managers focus outreach efforts on providing local residents with general information about black bears, as well as information pertinent to minimizing the potential for human–black bear conflict.

A process for comparing and interpreting differences in two benthic indices in New York Harbor

Often when various estuarine benthic indices disagree in their assessments of benthic condition, they are reflecting different aspects of benthic condition. We describe a process to screen indices for associations and, after identifying candidate metrics, evaluate metrics individually against the indices. We utilize radar plots as a multi-metric visualization tool, and conditional probability plots and receiver operating characteristic curves to evaluate associations seen in the plots. We investigated differences in two indices, the US EPA Environmental Monitoring and Assessment Programs benthic index for the Virginian Province and the New York Harbor benthic index of biotic integrity using data collected in New York Harbor and evaluated overall agreement of the indices and associations between each index and measures of habitat and sediment contamination. The indices agreed in approximately 78% of the cases. The New York Harbor benthic index of biotic integrity showed stronger associations with sediment metal contamination and grain size.

Beyond data management: how ecoinformatics can benefit environmental monitoring programs

We review ways in which the new discipline of ecoinformatics is changing how environmental monitoring data are managed, synthesized, and analyzed. Rapid improvements in information technology and strong interest in biodiversity and sustainable ecosystems are driving a vigorous phase of development in ecological databases. Emerging data standards and protocols enable these data to be shared in ways that have previously been difficult. We use the U.S. Environmental Protection Agency’s National Coastal Assessment (NCA) as an example. The NCA has collected biological, chemical, and physical data from thousands of stations around the U.S. coasts since 1990. NCA data that were collected primarily to assess the ecological condition of the U.S. coasts can be used in innovative ways, such as biogeographical studies to analyze species invasions. NCA application of ecoinformatics tools leads to new possibilities for integrating the hundreds of thousands of NCA species records with other databases to address broad-scale and long-term questions such as environmental impacts, global climate change, and species invasions.

CProb: a computational tool for conducting conditional probability analysis.

Conditional probability is the probability of observing one event given that another event has occurred. In an environmental context, conditional probability helps to assess the association between an environmental contaminant (i.e., the stressor) and the ecological condition of a resource (i.e., the response). These analyses, when combined with controlled experiments and other methodologies, show great promise in evaluating ecological conditions from observational data and in defining water quality and other environmental criteria. Current applications of conditional probability analysis (CPA) are largely done via scripts or cumbersome spreadsheet routines, which may prove daunting to end-users and do not provide access to the underlying scripts. Combining spreadsheets with scripts eases computation through a familiar interface (i.e., Microsoft Excel) and creates a transparent process through full accessibility to the scripts. With this in mind, we developed a software application, CProb, as an Add-in for Microsoft Excel with R, R(D)com Server, and Visual Basic for Applications. CProb calculates and plots scatterplots, empirical cumulative distribution functions, and conditional probability. In this short communication, we describe CPA, our motivation for developing a CPA tool, and our implementation of CPA as a Microsoft Excel Add-in. Further, we illustrate the use of our software with two examples: a water quality example and a landscape example. CProb is freely available for download at http://www.epa.gov/emap/nca/html/regions/cprob.

Ten Simple Rules for Digital Data Storage

Data is the central currency of science, but the nature of scientific data has changed dramatically with the rapid pace of technology. This change has led to the development of a wide variety of data formats, dataset sizes, data complexity, data use cases, and data sharing practices. Improvements in high throughput DNA sequencing, sustained institutional support for large sensor networks, and sky surveys with large-format digital cameras have created massive quantities of data. At the same time, the combination of increasingly diverse research teams and data aggregation in portals (e.g. for biodiversity data, GBIF or iDigBio) necessitates increased coordination among data collectors and institutions. As a consequence, “data” can now mean anything from petabytes of information stored in professionally-maintained databases, through spreadsheets on a single computer, to hand-written tables in lab notebooks on shelves. All remain important, but data curation practices must continue to keep pace with the changes brought about by new forms and practices of data collection and storage.

Predicting estuarine sediment metal concentrations and inferred ecological conditions: an information theoretic approach.

Empirically derived relationships associating sediment metal concentrations with degraded ecological conditions provide important information to assess estuarine condition. Resources limit the number, magnitude, and frequency of monitoring activities to acquire these data. Models that use available information and simple statistical relationships to predict sediment metal concentrations could provide an important tool for environmental assessment. We developed 45 predictive models for the total concentrations of copper, lead, mercury, and cadmium in estuarine sediments along the Southern New England and Mid-Atlantic regions of the United States. Using information theoretic model-averaging approaches, we found total developed land and percent silt/clay of estuarine sediment were the most important variables for predicting the presence of all four metals. Estuary area, river flow, tidal range, and total agricultural land varied in their importance. The model-averaged predictions explained 78.4, 70.5, 56.4, and 50.3% of the variation for copper, lead, mercury, and cadmium, respectively. Overall prediction accuracies of selected sediment benchmark values (i.e., effects ranges) were 83.9, 84.8, 78.6, and 92.0% for copper, lead, mercury, and cadmium, respectively. Our results further support the generally accepted conclusion that sediment metal concentrations are best described by the physical characteristics of the estuarine sediment and the total amount of urban land in the contributing watershed. We demonstrated that broad-scale predictive models built from existing monitoring data with information theoretic model-averaging approaches provide valuable predictions of estuarine sediment metal concentrations and show promise for future environmental modeling efforts in other regions.