Football Game Program 3 1965

Football Game Program 3 196

Appendix B. List of variables and their definitions for landcover, infrastructure, and census.

List of variables and their definitions for landcover, infrastructure, and census

Appendix A. List of variables and their definitions for natural environmental setting, soils, topography, and ecoregions.

List of variables and their definitions for natural environmental setting, soils, topography, and ecoregions

Appendix C. List of variables and their definitions for hydrology, water temperature, water chemistry and pesticide indices, SPMD chemistry, and habitat.

List of variables and their definitions for hydrology, water temperature, water chemistry and pesticide indices, SPMD chemistry, and habitat

Appendix D. List of variables and their definitions for invertebrate metrics.

List of variables and their definitions for invertebrate metrics

Appendix A. An example of a meta-analysis.

An example of a meta-analysis

Supplement 1. Computer programs in R and data sets used in the paper.

<h2>File List</h2><blockquote> <table> <tbody><tr> <td><a href="codewithcomments.txt">R code and comments</a></td> <td> </td> <td>Details of computer programs (in R) used in the paper.</td> </tr> <tr> <td><a href="N2Oemission.csv">N2O Emission</a></td> <td> </td> <td>Data for the N₂O emission Example.</td> </tr> <tr> <td><a href="rtol_MS.csv">rtol</a></td> <td> </td> <td>Richness tolerance data for the EUSE Example.</td> </tr> <tr> <td><a href="EUSE_NAT_ENV_MS.csv">EUSE_ENV_Data</a></td> <td> </td> <td>Data for the EUSE example, describing environmental characteristics of each metropolitan area.</td> </tr> <tr> <td><a href="City_AG_MS.csv">EUSE_AG_GRASS</a></td> <td> </td> <td>Data for the EUSE example, providing estimated background agricultural land use information.</td> </tr> </tbody></table> </blockquote><h2>Description</h2><blockquote> <p>R programs for the examples in the paper and in Appendix A are in <a href="codewithcomments.txt">this file</a>.</p> <p>The N₂O emission data set has seven columns:</p> -- TABLE: Please see in attached file. -- <p> </p> <p>The richness tolerance data set has eight columns:</p> -- TABLE: Please see in attached file. -- <p> </p> <p>Regional environmental data set has seven columns:</p> -- TABLE: Please see in attached file. -- <p> </p> <p>Background agricultural data set has five columns:</p> -- TABLE: Please see in attached file. -- </blockquote

Stream macroinvertebrate response models for bioassessment metrics: addressing the issue of spatial scale.

We developed independent predictive disturbance models for a full regional data set and four individual ecoregions (Full Region vs. Individual Ecoregion models) to evaluate effects of spatial scale on the assessment of human landscape modification, on predicted response of stream biota, and the effect of other possible confounding factors, such as watershed size and elevation, on model performance. We selected macroinvertebrate sampling sites for model development (n = 591) and validation (n = 467) that met strict screening criteria from four proximal ecoregions in the northeastern U.S.: North Central Appalachians, Ridge and Valley, Northeastern Highlands, and Northern Piedmont. Models were developed using boosted regression tree (BRT) techniques for four macroinvertebrate metrics; results were compared among ecoregions and metrics. Comparing within a region but across the four macroinvertebrate metrics, the average richness of tolerant taxa (RichTOL) had the highest R(2) for BRT models. Across the four metrics, final BRT models had between four and seven explanatory variables and always included a variable related to urbanization (e.g., population density, percent urban, or percent manmade channels), and either a measure of hydrologic runoff (e.g., minimum April, average December, or maximum monthly runoff) and(or) a natural landscape factor (e.g., riparian slope, precipitation, and elevation), or a measure of riparian disturbance. Contrary to our expectations, Full Region models explained nearly as much variance in the macroinvertebrate data as Individual Ecoregion models, and taking into account watershed size or elevation did not appear to improve model performance. As a result, it may be advantageous for bioassessment programs to develop large regional models as a preliminary assessment of overall disturbance conditions as long as the range in natural landscape variability is not excessive

Appendix A. Invertebrate responses to urbanization in the nine metropolitan areas based on EPT richness (EPT), ordination scores (NMDS), and organism tolerances (RICHTOL) summarized as linear and loess regressions and compared with change points defined by TITAN analysis.

Invertebrate responses to urbanization in the nine metropolitan areas based on EPT richness (EPT), ordination scores (NMDS), and organism tolerances (RICHTOL) summarized as linear and loess regressions and compared with change points defined by TITAN analysis