Barbara L. Jackson

A factor analysis of landscape pattern and structure metrics

Fifty-five metrics of landscape pattern and structure were calculated for 85 maps of land use and land cover. A multivariate factor analysis was used to identify the common axes (or dimensions) of pattern and structure which were measured by a reduced set of 26 metrics. The first six factors explained about 87% of the variation in the 26 landscape metrics. These factors were interpreted as composite measures of average patch compaction, overall image texture, average patch shape, patch perimeter-area scaling, number of attribute classes, and large-patch density-area scaling. We suggest that these factors can be represented in a simpler way by six univariate metrics - average perimeter-area ratio, contagion, standardized patch shape, patch perimeter-area scaling, number of attribute classes, and large-patch density-area scaling.

Scale problems in reporting landscape pattern at the regional scale

Remotely sensed data for Southeastern United States (Standard Federal Region 4) are used to examine the scale problems involved in reporting landscape pattern for a large, heterogeneous region. Frequency distributions of landscape indices illustrate problems associated with the grain or resolution of the data. Grain should be 2 to 5 times smaller than the spatial features of interest. The analyses also reveal that the indices are sensitive to the calculation scale,i.e., the unit area or extent over which the index is computed. This “sample area” must be 2 to 5 times larger than landscape patches to avoid bias in calculating the indices.

Monitoring environmental quality at the landscape scale

ver the past century, technological advances have greatly improved the standard of living in the United States. But these same advances have caused sweeping environmental changes, often unforeseen and potentially irreparable. Ethical stewardship of the environment requires that society monitor and assess environmental changes at the national scale with a view toward the conservation and wise management of our natural resources. Some of the most important environmental changes occur a t the spatial scale of landscapes. Obvious examples include clearcutting for lumber, urbanization, the loss of wetlands, and the conversion of forest and prairies into crop and grazing systems. Decisions about how to change land cover may be made by individual landowners, but their im-

The Collaborative Cross at Oak Ridge National Laboratory: developing a powerful resource for systems genetics.

Complex traits and disease comorbidity in humans and in model organisms are the result of naturally occurring polymorphisms that interact with each other and with the environment. To ensure the availability of resources needed to investigate biomolecular networks and systems-level phenotypes underlying complex traits, we have initiated breeding of a new genetic reference population of mice, the Collaborative Cross. This population has been designed to optimally support systems genetics analysis. Its novel and important features include a high level of genetic diversity, a large population size to ensure sufficient power in high-dimensional studies, and high mapping precision through accumulation of independent recombination events. Implementation of the Collaborative Cross has been ongoing at the Oak Ridge National Laboratory (ORNL) since May 2005. Production has been systematically managed using a software-assisted breeding program with fully traceable lineages, performed in a controlled environment. Currently, there are 650 lines in production, and close to 200 lines are now beyond their seventh generation of inbreeding. Retired breeders enter a high-throughput phenotyping protocol and DNA samples are banked for analyses of recombination history, allele drift and loss, and population structure. Herein we present a progress report of the Collaborative Cross breeding program at ORNL and a description of the kinds of investigations that this resource will support.

Genetic analysis in the Collaborative Cross breeding population

Genetic reference populations in model organisms are critical resources for systems genetic analysis of disease related phenotypes. The breeding history of these inbred panels may influence detectable allelic and phenotypic diversity. The existing panel of common inbred strains reflects historical selection biases, and existing recombinant inbred panels have low allelic diversity. All such populations may be subject to consequences of inbreeding depression. The Collaborative Cross (CC) is a mouse reference population with high allelic diversity that is being constructed using a randomized breeding design that systematically outcrosses eight founder strains, followed by inbreeding to obtain new recombinant inbred strains. Five of the eight founders are common laboratory strains, and three are wild-derived. Since its inception, the partially inbred CC has been characterized for physiological, morphological, and behavioral traits. The construction of this population provided a unique opportunity to observe phenotypic variation as new allelic combinations arose through intercrossing and inbreeding to create new stable genetic combinations. Processes including inbreeding depression and its impact on allelic and phenotypic diversity were assessed. Phenotypic variation in the CC breeding population exceeds that of existing mouse genetic reference populations due to both high founder genetic diversity and novel epistatic combinations. However, some focal evidence of allele purging was detected including a suggestive QTL for litter size in a location of changing allele frequency. Despite these inescapable pressures, high diversity and precision for genetic mapping remain. These results demonstrate the potential of the CC population once completed and highlight implications for development of related populations.

Sampling to characterize landscape pattern

Current reseach suggests that metrics of landscape pattern may reflect ecological processes operating at different scales and may provide an appropriate indicator for monitoring regional ecological changes. This paper examines the extent to which a 1/16 areal subset of the landscape using equally spaced 40-km2 hexagons can characterize the spatial extent of land cover types and landscape pattern (number of types of edges, patch shape complexity, dominance, and contagion). For 200-m resolution data the hexagon subset gives a reasonable estimate of overall landscape cover but may not be adequate for monitoring uncommon land cover types such as wetlands. For agriculture and forest, their proportion of the full landscape units is only outside the 95% confidence interval of the hexagon estimate 4–8% of the time, whereas the proportions for wetland and barren areas are outside the confidence interval 11–34% of the time. The hexagon subset also does not appear to be adequate as the sole basis for monitoring landscape pattern. The values for contagion, dominance, and shape complexity calculated on the full landscape units are outside the 95% confidence interval of the hexagon estimate 27–76% of the time. Other statistical analyses include regressions between full landscape and hexagon subsets, mean differences and standard errors along with tests on number of positive and negative values, and percent relative error of hexagon estimates.Although the research described in this article has been funded in part by the U.S. Environmental Protection Agency, under Interagency Agreement DW89934921-01-0 with the U.S. Department of Energy under Contract DE-AC05-84OR21400 with Martin Marietta Energy Systems, Inc., it has not been subjected to Agency review. Therefore, it does not necessarily reflect the views of the Agency. Mention of trade names or commercial products does not constitute endorsement or recommendation for use.

Understory vegetation indicators of anthropogenic disturbance in longleaf pine forests at Fort Benning, Georgia, USA

Abstract Environmental indicators for longleaf pine (Pinus palustris) ecosystems need to include some measure of understory vegetation because of its responsiveness to disturbance and management practices. To examine the characteristics of understory species that distinguish between disturbances induced by military traffic, we randomly established transects in four training intensity categories (reference, light, moderate, and heavy) and in an area that had been remediated following intense disturbance at Fort Benning, GA. A total of 134 plant species occurred in these transects with the highest diversity (95 species) in light training areas and the lowest (16 species) in heavily disturbed plots. Forty-seven species were observed in only one of the five disturbance categories. The variability in understory vegetation cover among disturbance types was trimodal ranging from less than 5% cover for heavily disturbed areas to 67% cover for reference, light, and remediated areas. High variability in species diversity and lack of difference in understory cover led us to consider life-form and plant families as indicators of military disturbance. Life-form successfully distinguished between plots based on military disturbances. Species that are Phanerophytes (trees and shrubs) were the most frequent life-form encountered in sites that experienced light infantry training. Therophytes (annuals) were the least common life-form in reference and light training areas. Chamaephytes (plants with their buds slightly above ground) were the least frequent life-form in moderate and remediation sites. Heavy training sites supported no Chamaephytes or Hemicryptophytes (plants with dormant buds at ground level). The heavy, moderate, remediated, and reference sites were all dominated by Cryptophytes (plants with underground buds) possibly because of their ability to withstand both military disturbance and ground fires (the natural disturbance of longleaf pine forests). Analysis of soils collected from each transect revealed that depth of the A layer of soil was significantly higher in reference and light training areas which may explain the life-form distributions. In addition, the diversity of plant families and, in particular, the presence of grasses and composites were indicative of training and remediation history. These results are supported by prior analysis of life-form distribution subsequent to other disturbances and demonstrate the ability of life-form and plant families to distinguish between military disturbances in longleaf pine forests.

MuTrack: a genome analysis system for large-scale mutagenesis in the mouse

BackgroundModern biological research makes possible the comprehensive study and development of heritable mutations in the mouse model at high-throughput. Using techniques spanning genetics, molecular biology, histology, and behavioral science, researchers may examine, with varying degrees of granularity, numerous phenotypic aspects of mutant mouse strains directly pertinent to human disease states. Success of these and other genome-wide endeavors relies on a well-structured bioinformatics core that brings together investigators from widely dispersed institutions and enables them to seamlessly integrate data, observations and discussions.DescriptionMuTrack was developed as the bioinformatics core for a large mouse phenotype screening effort. It is a comprehensive collection of on-line computational tools and tracks thousands of mutagenized mice from birth through senescence and death. It identifies the physical location of mice during an intensive phenotype screening process at several locations throughout the state of Tennessee and collects raw and processed experimental data from each domain. MuTracks statistical package allows researchers to access a real-time analysis of mouse pedigrees for aberrant behavior, and subsequent recirculation and retesting. The end result is the classification of potential and actual heritable mutant mouse strains that become immediately available to outside researchers who have expressed interest in the mutant phenotype.ConclusionMuTrack demonstrates the effectiveness of using bioinformatics techniques in data collection, integration and analysis to identify unique result sets that are beyond the capacity of a solitary laboratory. By employing the research expertise of investigators at several institutions for a broad-ranging study, the TMGC has amplified the effectiveness of any one consortium member. The bioinformatics strategy presented here lends future collaborative efforts a template for a comprehensive approach to large-scale analysis.